Individualized cancer treatment

ABSTRACT

Methods to formulate treatments for individual cancer patients by assessing genomic and/or phenotypic differences between cancer and normal tissues and integrating the results to identify dysfunctional pathways are described.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims benefit of U.S. Ser. No. 61/115,898 filed 18 Nov. 2008, the contents of which are incorporated herein by reference.

TECHNICAL FIELD

The invention relates to analysis of tumor tissue of individual patients to determine irregularities in gene expression that leads to identifying suitable treatments. Because each patient is individually analyzed for expression abnormalities, tailoring treatment protocols to the particular malignant tissue present in the patient is possible.

BACKGROUND ART

There is a plethora of known drugs sometimes used singly, but mostly in combination, for treating solid and blood tumors in humans. Chemotherapeutic approaches using small molecules such a vincristine, gemcitabine, 5-fluorouracil, and a litany of others (such as Gleevec®) which are used mostly in combination therapies, is widespread. In addition, biologicals such as Rituxan®, Erbitux™, and Herceptin® have also been used. With the exception of Herceptin®, and Gleevec® which targets an abnormal BCR-ABL protein found in patients with chronic myelogenous leukemia and a few others, these treatments are generally applied to individual patients based on guesswork rather than analysis. The heterogeneity of tumor types, even within a given organ such as breast or prostate, makes it difficult to ascertain in advance whether an individual patient's malignancy will, or will not, be responsive to any particular protocol. To applicants' knowledge, at least until recently, only the administration of Herceptin® was systematically based on the results of a companion diagnostic on an individual patient for an indication of whether (or not) the tumor will respond. More recently, other attempts to individualize treatment have been implemented, including chemosensitivity screening and tests for an individual target (e.g., KRAS mutations) which are used by some doctors. Estrogen receptor screening is often done routinely before administration of tamoxifen.

Studies have been done, however, with respect to tumor types in pools of many patient samples derived from a given organ or of cancers of a particular type to identify, in general, which metabolic or signal transduction/biological pathways are dysregulated in tumors of various types and which genes are over-expressed- or under-expressed. For example, studies of micro-RNA production patterns in ovarian cancer have been conducted by Dahiya, N., et al., PLoS ONE (2008) 18:e2436. Attempting to find such patterns on an individual basis has been limited to the recently reported sequencing of the entire genome of tumor cells from an individual patient at the cost of over $1 million, and as the patient had died before the project began, it too was not aimed at treatment of the patient herself. As the costs of sequencing have come down dramatically, a number of groups are conducting studies which attempt to sequence at least all of the open reading frames of genomes in cancer patients, comparing the sequences derived from tumor to those from normal tissue. The results of these studies are, at this point, unclear.

It would be extremely helpful to be able to formulate a treatment protocol for an individual patient based on the vulnerability of tumor cells in this patient to such a protocol as determined by the pathway-based irregularities which appear to be associated with the tumor. The present invention offers just such an opportunity.

DISCLOSURE OF THE INVENTION

The invention solves the problem of tailoring treatment protocols to individual cancer patients in a rational way by assessing the abnormalities effecting malignant growth in the tumor cells of the individual. By ascertaining the abnormalities in tumor cells as opposed to normal cells in the same individual, these abnormalities can be targeted in view of the availability of the many drugs whose target sites are already known.

Thus, in one aspect, the invention is directed to a method to identify a treatment target protocol in an individual cancer patient, which method comprises:

(a) ascertaining characteristics of the genome and/or characteristics of the molecular phenotype in a biopsy of the cancer afflicting said patient to obtain one or more first data sets and in normal tissue of said patient to obtain one or more second data sets;

(b) identifying differentiated characteristics in said one or more first data sets which differ from those in said one or more second data sets;

(c) ascertaining one or more pathways associated with said identified differentiated characteristics; and

(d) identifying at least one therapeutic target associated with said one or more pathways.

The method may further include designing a treatment protocol using drugs and/or biologics that interact with said at least one target.

The invention may further include administering the formulated treatment protocol to the patient and repeating the process after administration to determine whether the treatment had an impact and/or caused redundant pathways to operate. In addition, the treatment protocol formulated according to the method of the invention, may, in cooperation with the identified differentiated characteristics, be applied to discover further therapeutic and diagnostic methods appropriate for additional subjects.

In determining data sets related to the genome, among those characteristics that will be assessed are: presence of single nucleotide polymorphisms (SNPs), loss of heterozygosity (LOH), copy number variants (CNVs) and gene methylation, and sequence (full genome, full exome, or targeted). Multiple polymorphisms would, of course, also be included.

Characteristics which provide datasets for molecular phenotypes include overexpression or underexpression of open reading frames assessed either by RNA level or protein levels, and proteomic and activity analyses.

It is advantageous to diminish the misleading effects of noise in a single type of dataset by triangulating multiple data points to identify a biologically significant pattern (e.g., a pattern of over- and under-expressed genes consistent with the dysregulation of a specific pathway). It is often possible, as well, to extrapolate the results to characteristics that are not themselves measured, as illustrated below.

While it is possible to perform the method of the invention using only one type of characteristic or data set, such as over/underexpression of open reading frames, it is highly advantageous to use multiple types of data sets so that integration analyses can be performed taking advantage of redundancy of indications. Thus, using combinations of, for example, overexpression/underexpression with CNV/LOH databases not only provides an increased level of confidence in the results, but also allows correlations to come to light leading to hypothesize pathways that might not otherwise be seen.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a graph showing cellular functions enriched in regulated genes from primary colon tumor sample.

FIG. 2 is a graph showing the cellular functions enriched in regulated genes from a liver metastasis sample derived from the same patient whose colon cancer is diagrammed in FIG. 1.

FIGS. 3A-3B show a diagrams of protein interaction networks deduced by the method of the invention from (FIG. 3A) primary colon tumor and (FIG. 3B) liver metastasis.

FIG. 4 is a diagram of HDAC-Hsp cellular interactions as described in the art.

FIG. 5 shows the cellular functions of the regulated genes in the tumor sample from a patient with lung cancer.

FIG. 6 is a diagram of the PDGF pathway known in the art but with superimposed gene expression data. The upper line represents the cell membrane and the lower horizontal line represents the barrier between the cytoplasm and the nucleus. The genes with darker shading are up-regulated and those with lighter shading are down-regulated in the tumor sample.

FIG. 7 is a diagram of the prior art knowledge of the role of PDGF pathways in tumor biology.

FIG. 8 is a diagram of what is known in the prior art of the VEGF pathway and its role in angiogenesis.

FIG. 9 is a graph showing cellular functions enriched in the regulated genes from tumor samples of a patient with liver cancer.

FIG. 10 shows the network containing members of the angiotensin pathway as described in the art.

FIG. 11 shows the network containing the NOX1 gene as described in the art.

FIG. 12 is a graph showing cellular functions enriched in regulated genes from a melanoma sample.

FIG. 13 is a diagram of the cell cycle and the relevance of CDK2.

FIGS. 14 and 15 are more detailed diagrams of the cell cycle shown in FIG. 13.

FIG. 16 is a diagram showing ERK-MAPK signaling and the role of Src.

FIG. 17 is a diagram of SAPK/JNK signaling showing the role of LCK.

FIG. 18 is a graph showing cellular functions enriched in aberrant genes from a melanoma sample.

FIG. 19 is a diagram of the signaling pathway associated with B-Raf.

FIG. 20 is a diagram showing the pathways associated with PTEN down-regulation.

MODES OF CARRYING OUT THE INVENTION

The method of the invention takes advantage of the exponential growth of knowledge related to gene expression, metabolic pathways, signal transduction pathways, the nature of drug targets, and cell regulation systems that has accumulated over the past half century, as well as techniques for organizing this information, and accessing multiplicities of data points in a relatively economic fashion. A number of canonical pathways are already postulated and understood in the art based on isolated findings reported in the literature, but many more pathways remain to be elucidated by assembling and evaluating these data. By virtue of obtaining and triangulating large numbers of data points for an individual patient within and across datasets, applicants are able to overcome the inherent noise from a measurement of few samples and provide a road map of the abnormalities associated with tumor cells as opposed to normal cells in the patient and thus formulate treatment regimens targeting the components of these irregularities.

The method of the invention begins by obtaining suitable biopsy samples from the patient. The biopsy is obtained using standard methodology; at least one sample, but preferably three (which allows calculation of a p-value), is obtained from tumor tissue and another sample but preferably three, is obtained from normal tissue in the same individual. The individual may have a primary solid tumor of an organ such as liver, kidney, stomach, bone, lung, pancreas, prostate, breast, ovary, bladder, eye, skin, or other specific location whereby an intact sample of small dimension can be obtained readily. Alternatively, the tumor cell may be distributed throughout the bloodstream as is the case for leukemias or lymphomas and the tumor cells are then recovered from and separated from normal cells in the blood. Tumor markers have also been found in bodily fluids in general such as saliva and serum. The primary solid tumor may also have metastasized and samples of metastatic tissue should also be obtained and assayed according to the method of the invention. In one embodiment, normal cells are obtained from similar tissues in which the primary tumor is found in the same individual who provides the tumor sample. Alternatively, normal tissue from the organ to which it has metastasized could be used as the comparative standard. If normal tissue from the same patient is not available, expression levels of various genes in normal tissues are available from databases such as GEO, maintained by the NCBI, as well as several maintained by companies (e.g., Gene Logic). One advantage of using the patient's own normal tissue is that the standard permits taking account of any drugs that may be in the system of this patient, such as chemotherapeutic drugs that have already been administered, as well as individual biological variability.

In some cases, the normal tissue may contain substantial numbers of stromal cells which are cells that support the tumor itself and which may distort the comparison. These stromal cells are connective tissue cells and are associated with a number of organs to support the functional cells in bodily organs. Stromal cells include fibroblasts, immune cells and endothelial cells, among others. In particular, if the normal tissue contains stromal cells, the methods described below to further validate the results of the method are particularly important.

The biopsy samples are preserved by methods standard in the art, such as flash-freezing or by embedding them in paraffin and fixing them with formalin.

Next, the relevant cellular components are extracted. For analysis of a molecular phenotype, expression levels may be measured using, for example, level of mRNA. The mRNA is extracted and isolated using standard techniques. The extracted mRNA is then assessed using whole genome microarrays, many of which are commercially available from Affymetrix, Illumina, or Agilent, for example, or is obtained through whole exome sequencing. The results from the microarray analysis provide data which show expression levels of a multiplicity of genes in tumor derived tissue and normal tissue.

Comparison for each gene of its level of expression in tumor and normal tissue permits an assessment of overexpression or underexpression of each gene in the tumor tissue as compared to normal. Any useable level of differentiation can be employed, for example, only genes that are expressed at a level of 10-fold, 5-fold, 3-fold or 2-fold differences may be considered. Within the same comparison, if desired, different differential standards may be employed for different groups of genes. It is not necessary to use hard cutoffs, and fadeout techniques can also be employed. Differing levels of p-values may also be employed to filter the gene list, depending on the context.

For metastasized tissue, the normal control may include either the primary organ or the organ which is the location of the metastasis; both can be employed if desired. Further, if there are multiple metastases, each one may have a different pattern.

In addition to assessing expression levels using mRNA as an index, the levels of protein present may be assessed using, for example, standard proteomic techniques or immunoassays. The activity of various proteins may also be assessed using standard techniques. The nature of the analysis of molecular phenotype is not critical—any method to obtain information about aspects of the phenotypic characteristics that may be relevant to the cancer could be used.

For determination of genomic characteristics, chromosomal tissue is extracted from the biopsy samples and analyzed, for example, for the presence of SNPs that are characteristic of the tumor tissue as compared to normal. The analysis also involves microarrays, and microarrays for SNP analysis are also available commercially. There may also be multiple copies of the certain genes and the commercially available SNP arrays are able to provide information concerning copy number (CNV), as well as the presence of the SNP itself. This information can also be obtained through full-genome sequencing, or other designed sequencing approaches.

The identification of one or more SNPs in the tumor tissue as compared to normal tissue, and the copy number thereof, as well as loss of heterozygosity (LOH) or methylation patterns also provide information as to the pathway irregularities that might be found in the tumor tissue. Multiplicities of SNPs provide further information along these lines. In one embodiment, information regarding copy number of genes in tumor tissue may be combined with the data on overexpressed and underexpressed genes to result in additional data points that add to the accuracy of the analysis. Thus, since a single patient is being evaluated, the availability of more data points provides an opportunity to distinguish signal from noise, by noting convergent results. Using, for example, the combination just of SNP data and expression data, there may be 20-50 or more data points supporting a given therapeutic hypothesis (pathway members×SNP/CNV).

As illustrated below, the data obtained are used as a basis for determining which pathways in the tumor cell are abnormal. The pathways may be metabolic pathways or signal transduction pathways or any other group of interacting proteins which may affect cellular functioning. The pathways may either be those already known and described in textbooks, or may be assembled from curating the primary literature. This curatorial activity and assembly into putative pathways has already been accomplished in many instances and algorithms for fitting aberrant genes, such as overexpressed and underexpressed genes, into such pathways are available from, for example, Ingenuity, GeneGo, and Pathway Assist. These algorithms are only available for expression data, so other types of data (copy number, mutation, etc.) are inserted into Ingenuity with a specific fold change—e.g., 1,000—used as a “flag” to identify them. The resultant data are then used for visualization purposes to identify pathway hypotheses, and are later removed and adjusted to be denoted by other means for the purpose of elucidating the pathways/discussing them. These tools may be supplemented by curatorial activities practiced by the diagnostician and assembled in the diagnostician's own database. This latter possibility is particularly important since SNPs occurring in tumors may not necessarily be represented in commercially available microarrays.

Clearly, the complexity of the correlations and algorithms required for determining relevant pathways requires the use of software and computer manipulations.

The dysregulated pathways identified using these techniques are assessed by several criteria, including

1. whether the pathway contains a sufficient number of independent data points to overcome the statistical limitations of having few samples;

2. whether the pathway exhibits a coherent pattern, for example, if gene products involved are consistently upregulated or downregulated in accordance with the performance of the pathway itself;

3. whether the pathway is plausibly disease-relevant—e.g., whether there are reports in the literature that the pathway may be somehow linked to malignancy;

4. whether the pathway contains targets that correspond to approved or investigational drugs or biologics that can be used to mitigate the irregularity in the pathway; and

5. whether the pathway is validated by confirmatory evidence from data obtained from several types of characteristics.

Of course, not every one of the above five criteria need be met. However, if a protocol is to be formulated, there must be known or investigational drugs or biologics that can target the components of the pathways identified.

A single type of data, such as overexpression/underexpression may be employed when necessary, but it is beneficial when possible to combine information concerning differential characteristics obtained according to these criteria with differentiated characteristics obtained using genomic information such as SNPs or CNV or both, or with other types of data sets, to that integration techniques can be employed.

The integration of more than one type of data is illustrated below in Examples 4 and 5. By integrating data from more than one type of determination (e.g., expression levels and genomic data) targets and treatments are suggested that would not have been evident by the use of one data set alone.

This latter confirmatory data employs analysis of either the function of the individual genes identified in the pathway or the genes that are simply on the list. If these genes are known to provide functions that are reasonably related to cell proliferation of abnormal growth, this further confirms the validity of the list and the projected pathway. If the pathway contains genes that exhibit SNPs in the tumor tissue and altered copy number, this further validates the relevance of the pathway.

Triangulation and Integration

As noted above, because multiple observations are obtained, their collective implications will permit deduction of the existence of characteristics that have not been directly measured. This can be considered as one manifestation of “triangulation” and/or “integration” which permits such inferences to be drawn.

For instance, where copy number and expression data indicate possible enhanced EGFR activity and thus hyperphosphorylation, this is inferred by data showing that the AT1R pathway was dysregulated. It is known that AT1R transactivates EGFR. It is inferred from an indication that EGFR is being degraded at a slower rate than normal, and the receptor not being desensitized as much as it would ordinarily be because p38 is downregulated, and one of its functions is to desensitize and degrade EGFR.

Thus, by correlating the results of multiple data points and multiple types of analysis, greater assurance is provided that the measured parameters are significant (as discussed further below) and further leads to additional conclusions that could not be drawn simply from the actual measured data points.

In addition, the noise level is managed by triangulation methods (as discussed in further detail below).

The term “triangulation” as used in the present application refers to assembling multiple individual items of data into meaningful compilations based on the known interrelationships of the components for which each data point is obtained. Thus, data with respect to individual members of a known pathway, for example, are assembled so that mutually supportive inferences enhance the probability that the pathway itself is aberrant or not aberrant in a tumor sample. By virtue of assembling these data in an orderly fashion, the significance level of the conclusion suggested by the data is enhanced. This is essentially a way to obtain meaningful results against a noisy background, as discussed further in the following paragraph and in the algorithms described below.

Management of the noise problem has traditionally been done by using a large cohort of patients/data/samples, and averaging them. While one may not trust every gene of every sample, the average is much more trustworthy. For example, when using microarrays, a significance value of p<0.05 for an individual gene is not particularly valuable. The reason is that there are 40,000 genes on the chip, so one would expect that 40,000×0.05=2,000 genes that would show as p<0.05 purely on a random basis. Typically a correction factor is used, multiplying the p value by the number of genes (“false discovery” or Bonferroni correction), so that one would need a p<0.00001 on a specific gene to have a 5% overall chance that the data are correct. Current technology does not provide such sensitivity for a specific sample, so the usual approach is to average over numerous data sets, to lower the p value to this threshold, as well as often to predefine a limited set of genes (e.g., a few hundred) that one is interested in to reduce the magnitude of the correction and lower the p-value. Even then it is hard to achieve significance, which means that further validation studies that focus on the single gene of interest are necessary to establish significance for it.

In another example, for microarray data with 20,000 known genes, to be certain that a given gene is statistically significant to the p=0.05 level requires a p-value on the individual genes of p=2×10⁻⁶. Obtaining p=0.05 on the single gene would require 1×10¹³ samples. One approach is to restrict the sample to a set of candidate genes, for example, to only 100 out of the 20,000. This would mean a p-value of only p=0.0005 for each gene corresponding to 10,000 samples. These calculations use the Bonferroni correction of p_adj=Np, where N is the number of genes being examined, and the standard error of the mean formula, p_mean=p/sqrt(n) where n is the number of samples.

The approach in the prior art is to use a set of samples for discovery of the gene, and then a set of samples used for validation, focused only on the gene of interest. As many genes will “appear” attractive on the training set, the odds of them validating in the validation set are low. Thus, these approaches work with larger databases, but not with only a few samples.

When working with a single patient, however, this cannot be done—the data are limited to a very small number of samples (e.g., 3 replicates), much too small to use this approach. Previous attempts to overcome this required a great deal of experimental/wetlab work to validate results for every gene of interest, and require use of a single platform. Changing arrays, or using FFPE samples instead of flash frozen, would require repeating all of these experiments.

The present invention achieves these goals by examining dysregulation at the functional/pathway level. If only the gene level is examined, it is important to know whether or not that gene is really overexpressed. At the pathway level, if there is activity among a set of 10-15 different genes, it matters less whether any one gene is really overexpressed. For example, in validating the results by IHC, IHC would not be needed on each of the specific genes that were upregulated to see if they, one by one, were upregulated. Rather, IHC is done on as few or as many elements of the pathway as desired/practicable, to see if a significant number of them were affected. Even if they were different pathway elements, the conclusion—that this pathway is activated—would be the same (provided, of course, that the data are broadly consistent—i.e., inhibition vs. activation should be the same).

This approach lowers the possible number of “discoveries” from the number of genes on the chip to the number of pathways that are possibly dysregulated leading to a possible treatment—a few hundred, instead of tens of thousands. One could ask questions like: if there is a pathway consisting of q elements, how many would have to be dysregulated at the p<0.05 level to have a p<0.05 that the pathway is activated? Algorithms that answer this question are discussed further below and are one of the ways (but only one) by which hypotheses are judged.

For example, to establish with 95% confidence that a pathway was activated by looking at several genes simultaneously, with only 3 samples, if the pathway had 10 elements only 5 of those would need to have 95% confidence; with 20 elements in the pathway, only 7; and with 40 elements, only 9. This calculation assumes approximately 283 different possible pathways (the number of canonical pathways in Ingenuity); as calculated by the statistical algorithm described below. Thus, by looking at pathways rather than individual genes one greatly reduces the number of possible discoveries, and therefore the multiple correction factor; this method requires several genes to be expressed simultaneously. The math works very similarly when triangulating with different technologies.

This method basically devalues the contribution of any one gene, so that inability to establish significance based on a single gene is not fatal. As shown by the statistical algorithm below—a single gene with an incredibly low p value or a large set of genes with moderately low p-values could both yield a significant result, but these are not needed for the invention methods to succeed. Instead of getting large amounts of data by looking at the same gene across a cohort, the present method provides data by looking at related genes within the same patient to achieve significance.

Three different algorithms have been developed to evaluate the significance of an identified pathway hypothesized on the basis of the analyzed data to furnish a target for a therapeutic to analyze them in 3 different ways. Modifications of these to account for any negative data are described as well.

Algorithm 1 is used, independent of the p values of specific data, to determine how many pathway elements must be dysregulated at the 95% confidence level (for instance) to have an overall 95% confidence that the pathway is active.

Algorithm 2 inputs the pathway data—the exact p-values of the various elements—and calculates a p-value of the overall pathway.

Algorithm 3 introduces the concept of “privileged data.” For instance, to conclude that the angiotensin pathway is activated, dysregulation of all pathway elements is helpful, but specifically dysregulation of angiotensin and its receptor are more helpful than other components, and this can be reflected in the statistics.

Algorithms 1 and 2 are focused on measuring strength of the hypothesis that the pathway is involved, while algorithm 3 adds biological reasoning that might be useful in identifying hypotheses that are likely to yield fruit in the real world. Additional elements of biological reasoning (such as the “privileged” element concept) can be added incrementally to determine whether they can reproduce the conclusions of a more subtle/sophisticated human interpreter, and if they don't, how further to augment them. Each refinement will add a tunable parameter, which will have to be determined experimentally, and so the more complicated the reasoning, the more data will be required to test/tune the parameter. Algorithms 1 and 2 have no tunable parameters (merely measuring), and algorithm 3 has one tunable parameter (beyond measurement, to prediction).

Π=1−{1−[Σ_(k=n) ^(q)(q|k)(1−p)^((q−k)) p ^(k)]}^(N)  Algorithm 1a

Where Π=probability that pathway is not noise, p is the threshold probability for a single gene (usually 0.05), n is the number of pathway elements that are dysregulated/amplified/mutated in a manner consistent with the hypothesis, q is the total number of elements in the pathway, N is the total number of possible pathways that might be considered, and (q|k) is q!/k!(q−k)!

Example of utility: If there are, say, 200 possible pathways to consider, then to get a 95% confidence level associated with the pathway activation (after correcting for multiple pathways) a pathway of 10 elements would require 6 of those elements be significant to p=0.05, a pathway of 20 elements would require 7, a pathway of 40 elements would require 9, etc.

Proof: We define Π as the probability that, for any one of N pathways, at least n elements out of a possible q have probability <p.

Let Π′ be the same as Π but with respect to a single pathway. Then:

Π=1−{1−Π′}^(N)

which reduces to the normal Bonferroni correction for small Π′.

The probability of exactly k elements out of q being dysregulated with probability <p is given by the binomial expansion

(q|k)(1−p)^((q−k))p^(k)

and so Π′ is given as

Π′=Σ_(k=n) ^(q)(q|k)(1−p)^((q−k)) p ^(k)

leading to algorithm 1.

Π=1−{1−(q|n)(1−p _(T))^((q−n))Π_(k=1) ^(n) p _(k)}^(N)  Algorithm 2a

Where Π=probability that pathway is not noise, p_(T) is the threshold probability for a single gene (usually 0.05), p_(k) is the p-value for a given gene k, where the genes are ordered from lowest p-value to highest p-value, n is the number of pathway elements that are dysregulated/amplified/mutated, q is the total number of elements in the pathway, N is the total number of possible pathways that might be considered, and (q|n) is q!/n!(q−n)!

Example of utility: The previous formula just considers the case of p=0.05. However, if we have one or more genes with specific p-values that are much smaller, fewer genes may be required to conclude significance.

Proof: We define Π as the probability that the probability of a given gene <p for the specific n elements 1, . . . n, with probabilities p₁, . . . p_(n).

As in algorithm 1a, let Π′ be the same as Π but with respect to a single pathway. Then:

Π=1−{1−Π′}^(N)

Here, Π′ is the probability of exactly n elements out of q being dysregulated with the specific probabilities p₁, . . . p_(n), all <p_(T) while the other q−n elements have p>p_(T). This is given by

Π′=(q|n)(1−p _(T))^((q−n))Π_(k=1) ^(n) p _(k)

where the factor (q|n) derives from the fact that the n significant genes and the q−n non-significant genes could be reordered arbitrarily without changing the result.

Putting these two equations together leads to algorithm 2a.

Π=1−{1−[(q+μ|n+τ)(1−p _(T))^((q−n))Π_(k=1) ^(n) p _(k)/(μ|τ)]^((1−β))[(μ|τ)(1−p _(T))^((μ−τ))Π_(r=1) ^(τ) p _(r)]}^(N)  Algorithm 3a

Where Π=probability that pathway is activated, p_(T) is the threshold probability for a single gene (usually 0.05), there are μ privileged elements, of which τ have p<p_(T) and there are q non-privileged elements, of which n have p<p_(T). p_(k) is the p-value for a non-privileged gene k, and similarly p_(r) is the p-value for a privileged gene r. β is the degree of privilege, with 0≦β≦1, with β=0 meaning that there is no privilege (i.e., the elements μ have the same importance as the elements q), and conversely β=1 means that the privilege is complete, i.e., the q elements are essentially irrelevant to concluding whether the pathway is activated. N is the total number of possible pathways that might be considered, and (q|n) is q!/n!(q−n)!

Example of utility: In cases where the VEGF pathway appears to be activated (in the sense that the data is inconsistent with the null hypothesis of the data being noise) as determined by algorithms 1a and/or 2a. Biologically, however, a hypothesis in which the VEGF ligand and receptor are specifically dysregulated will carry more weight than those where more ancillary genes are dysregulated, though the ancillary genes are still important to concluding the relevance of the pathway. In this case, μ=2 (the two privileged genes) and β represents the magnitude of the relative importance of these genes over the others.

Rationale: Using similar logic as for algorithm 2a, we can define the probability that the patterns seen in the privileged genes are noise by

[(μ|τ)(1−p _(T))^((μ−τ))Π_(r=1) ^(τ) p _(r)]

which should be the result for β=1 (only privileged genes matter), where for β=0 (privileged genes and non-privileged are of equal importance), the result should reduce to:

(q+μ|n+τ)(1−p _(T))^((q+μ−n−τ))Π_(k=1) ^(n) p _(k)Π_(r=1) ^(τ) p _(r)

i.e., the same result as if we had never selected out the privileged subgroup μ. The algorithm above reduces to these two extreme cases directly and has other critical properties such as decreasing the contribution of the non-privileged genes monotonically as β increases, being a continuous function of β, etc. Since “degree of privilege” has no independent definition between 0 and 1, any other formula that has these properties can be made equivalent to this one by reparametrizing β. Since the “correct” value for β will have to be measured experimentally in order to most closely reproduce human judgment about the importance of this privilege, all parametrizations of β are of equal value and therefore the formula above represents a correct approach to capturing this level of judgment. This particular parametrization has the property that the log of the probability is a linear function of β, interpolating between the two extremal results.

Even a formula that is non-monotonic in β would technically still be acceptable if β is tuned experimentally, but the results would be more difficult to interpret/understand on an intuitive level.

In the event that there are negative data (i.e., contradictory data that meet the standard of significance, usually 0.05), these algorithms are modified to take this into account, as set forth in algorithms 1b, 2b, and 3b.

To account for negative data, the above algorithms should be modified to

Π=1−{1−Π₊/(Π₊+Π⁻−Π₊Π⁻)}^(N)

where, Π₊ and Π⁻ are defined in the following algorithm-specific ways:

Π₊=[Σ_(k=n) ^(q)(q|k)(1−p)^((q−k)) p ^(k)] and Π⁻=[Σ_(k=n′) ^(q)(q|k)(1−p)^((q−k)) p ^(k)]  Algorithm 1b

where n′ is the number of statistically significant aberrant genes that are evidence against the hypothesis and all other definitions are as in Algorithm 1a.

Π₊=(q|n)(1−p _(T))^((q−n))Π_(k=1) ^(n) p _(k) and Π⁻=(q|n′)(1−p _(T))^((q−n′))Π_(k=q−n′) ^(q) p _(k)  Algorithm 2b

where n′ is defined as in Algorithm 1b, all other definitions are as in Algorithm 2a, and the genes are ordered so that the first n genes are those statistically that are statistically significant in support of the hypothesis, the next q−n−n′ genes that have no statistically significant measurement are next, and finally the n′ genes that have evidence against the hypothesis are listed.

Π₊=[(q+μ|n+τ)(1−p _(T))^((q−n))Π_(k=1) ^(n) p _(k)/(μ|τ)]^((1−β))[(μ|τ)(1−p _(T))^((μ−τ))Π_(r=1) ^(τ) p _(r)] and

Π⁻=[(q+μ|n+τ′)(1−p _(T))^((q−n′))Π_(k=1) ^(n′) p _(k)/(μ|τ′)]^((1−β))[(μ|τ′)(1−p _(T))^((μ−τ′))Π_(r=1) ^(τ′) p _(r)]  Algorithm 3b

where n′ the number of negative data points among the non-privileged genes, τ′ is the number of negative data points among the privileged genes, and all other definitions are as in Algorithm 3a.

Proof:

In the case of each of these three algorithms, Π₊ represents the probability of the hypothesis being false (ignoring the negative data points) while Π⁻ represents the probability of the opposite hypothesis being false (ignoring the positive data points), as per the proofs in each of the 3 previous cases. H₊ and H⁻ are defined as the probabilities of the hypothesis being true and the opposite hypothesis being true, respectively. There are four hypotheses that capture the possible space:

-   -   H₁: H₊ is true and H⁻ is false     -   H₂: H₊ is false and H⁻ is true     -   H₃: H₊ and H⁻ are both false     -   H₄: H₊ and H⁻ are both true

By Bayes' rule, the probability of each hypothesis being true, given the data observed, is

P(H _(i)|data)=P(data|H _(i))P(H _(i))/Z, where Z=Σ _(j=1) ⁴ P(data|H _(j))P(H _(j)).

Under the null hypothesis (that the data are simply noise), the data sets are independent, so we have

P(H ₁)=(1−Π₊)Π⁻

P(H ₂)=Π₊(1−Π⁻)

P(H ₃)=Π₊Π⁻

P(H ₄)=(1−Π₊)(1−Π⁻)

Usually, P(data|H_(i))=1, because the data were observed, in which case the Bayes' rule above reduces to the tautology

P(H _(i)|data)=P(H _(i))/Z, where Z=Σ _(i=1) ⁴ P(H _(i))=1.

However, since H₄ is not internally consistent (two hypotheses contradicting each other cannot formally be true),

P(data|H ₄)=0.

Then Bayes' rule reduces to:

P(H _(i)|data)=P(H _(i))/Z, where Z=Σ _(j=1) ³ P(H _(j)) for i=1, 2, or 3, and P(H ₄|data)=0

or specifically, for i=1, plugging in the above gives:

P(H ₁)=(1−Π₊)Π⁻/(Π₊+Π⁻−Π₊Π⁻).

Since the p-value sought is the probability that H₁ is false, before correcting for multiple pathways this is

1−P(H ₁)=Π₊/(Π₊+Π⁻−Π₊Π⁻).

Applying the correction for multiple pathways as in the previous proofs yields the algorithm.

In addition to validating the pathway hypothesized by triangulation as determined using the algorithms above, “integration” also allows more meaningful appreciation of real results against a noisy background, but rather than applying the algorithms to a coherent data set, combines data from multiple technologies, that aren't necessarily used to “playing together”, so that they can be viewed and considered simultaneously within the functional biology. For example, the formats for the CNV data are very different from those of the expression data; mutation data are yet different from either. To conclude, “the expression says this, the copy number says that, and they are or are not consistent,” is straightforward. It is more difficult to perceive that “Here is something that wouldn't have caught my attention if I were looking individually at either of the data sets, but when looking at them together in the same picture, I see it clearly.” For one patient, for instance, before the data were integrated, no hypotheses were found. After building the tools (such as assigning arbitrary values for results in one data set into calculations designed for another data set as described in paragraph [0042]) to integrate them, three good hypotheses emerged. Integration thus has a technical (IT) component and a strategic component to it.

Results

The data and results obtained from an individual patient reveal the relevant disease biology, ties the biology to drugs, and these drugs are tested in the patient. For drugs that work in the individual patient, other patients who suffer that cancer or other cancers, and have the same critical elements are likely to respond the same way to the therapy. A validation study is done to confirm this.

Thus, drugs may include small molecules—e.g., conventional drugs such as Gleevec®, doxorubicin, carboplatin and the like, including investigational drugs. Biologics such as antibodies, therapeutic proteins, gene therapy systems, stem cell therapy systems, and the like, including those in investigational studies may also be used. Use of antibody therapy in tumor treatment is becoming conventional, as is treatment with cytokines and other proteins. Our approach has the ability to exploit the entire pharmacopeia of ˜2500 approved drugs as well as investigational agents currently in trials. to engineer an effective customized treatment.

Using the dysregulated pathway information, treatment protocols using drugs or biologics are then proposed and formulated. A database of compounds/biologics/therapies is maintained together with known and suspected targets so that these can be compared to the pathways to determine which protocols are most effective. This is particularly useful in proposing combination therapies, as multiple components of the pathway may be targeted by a multiplicity of components of the protocol.

By utilizing the analysis described herein, the probability of success in treating an individual patient is greatly improved, and the formulated protocol may then be administered to the patient. Routine optimization will determine mode of administration, timing, dosage levels and other parameters relevant to the particular treatment.

In some cases, additional validation studies may be suggested to provide further evidence for the explicated hypotheses. These may include, but not be limited to, studies to assess phosphorylation or other mode of activation of cellular proteins, assessment of mutation status of individual genes, screening of drugs against tumor-derived cells, or various other cell or molecular biology-based assays.

While intuitively it would seem better to analyze a database to look for appropriate targets, that may not be the case. As there are often hundreds of subtypes within a given cancer type, and the search on the database will generally only give information either on the most prevalent subtypes or will give “high level” information. The methods of the present invention give information on rare subtypes, and very “fine-grained” information.

Patients on whom the invention methods are conducted may be those who have failed multiple lines of therapies that have been approved based on results in trials—which by definition focus on the prevalent subtypes, and thus are likely to have rare subtypes. Diagnostics relevant to a rare subtype can be as valuable as those relevant to a common subtype; for example, cKIT mutation in melanoma is only present in 3% of melanomas, but when it is present, Gleevec® is highly effective, so all melanoma patients should be tested for cKIT despite its relative rarity.

The distinction between “high level” vs. “fine grained” information can best be understood by the following example. One distinction among colon cancer patients is whether they have a mutated or wild-type EGFR. This was a test originally used to predict responsiveness to Erbitux™ (two “subtypes”). Later studies revealed that a mutated KRAS predicted a poor responsiveness to Erbitux™ whether or not EGFR is mutated. Both tests are now used in combination (or KRAS alone) to determine susceptibility to Erbitux™. Two subtypes have thus now been split into more. 70% of patients with EGFR mutant, KRAS wild-type, respond to EGFR inhibitors. So there is yet another reason why this is not 100% to be discovered in the future. This will split this subtype into 2 (or more) again where one is yet further enriched for EGFR inhibition response. So, there may be 100 subtypes, with this representing 10 of them, for illustration. The present method, nucleating around a single case rather than a database search, is more likely to distinguish a single subtype from the other 99 rather than a higher-level grouping. In principle, our approach can be used to discover clinically significant subtypes, such as EGFR mutant cancers with mutated KRAS, in a single individual. If validated in other patients, these new subtypes can become valuable additions to the high level databases and standard panels of point mutations used to stratify patients.

The following examples are offered to illustrate but not to limit the invention.

EXAMPLE 1 Formulation of Treatment for Colon Cancer in a Patient

Colon tumor tissue and colon normal tissue, as well as tissue from a liver metastasis from an individual patient were biopsied and subjected to Affymetrix transcription profiling. Ratios of gene expression (mRNA levels) from the primary colon tumor and liver metastasis samples, both relative to normal colon tissue samples were determined. Genes with an expression ratio threshold of 1.8-fold up- or down-regulation, and a significance P-value of 0.05 in malignant relative to normal cells were identified as 288 genes from the colon tumor and 348 genes from the liver metastasis.

Using a tool provided by Ingenuity Systems, the identified genes were subjected to an algorithm which finds highly interconnected networks of interacting genes (and their corresponding proteins). Ingenuity's algorithm for constructing a network graph is based on hand-curating protein/protein interactions (as defined by physical and/or functional interactions) directly from the research literature. In each individual analysis, the Ingenuity algorithm compares the regulated genes to this underlying master network and clusters of proteins that have multiple mutual interactions are identified and presented as smaller sub-networks. The resulting pathways can be directly supported by references to the literature both within the Ingenuity tool, and independently. [Similar algorithms are in use by other tools (e.g., those by GeneGo, and one in the public domain); we use Ingenuity because their database of curated literature is currently the most comprehensive, but the work is not conditioned on them specifically.] These findings were then further analyzed independently of the Ingenuity tool, to find particularly relevant pathways which could provide potential therapeutic targets.

An initial analysis was done to confirm that the global gene expression from the tumor sample was generally consistent with a priori expectations for neoplasms of this type. The networks that were assembled by the protein interaction algorithm from the list of up- or down-regulated genes were analyzed with respect to the cellular and organismal functions of their individual component genes. From the primary colon tumor sample, the four top-scoring networks (with respect to the interconnectedness of their component genes) were greatly enriched in the following functions:

Cancer Cellular Movement Cell Morphology Neurological Disease Protein Synthesis Connective Tissue Disorders Gastrointestinal Disease Cell Signaling Carbohydrate Metabolism Molecular Transport Small Molecule Biochemistry

From the liver metastasis sample, the four top-scoring networks (with respect to the interconnectedness of their component genes) were greatly enriched in the following functions:

Genetic Disorder Hematological Disease Ophthalmic Disease Lipid Metabolism Small Molecule Biochemistry Protein Synthesis Hematological System Development Organismal Functions and Function Infectious Disease Post-Translational Modification Protein Folding Cancer

This overall pattern is consistent with what one might expect from the global gene expression of a tumor sample, as compared to normal tissue. This helps to confirm that the differently expressed genes are from the tumor sample and that the integrity of the gene expression milieu has been maintained. The entire list of networks and their associated functions, from both samples, is set forth at the end of this Example.

Both entire lists of differently expressed genes were scored for associated cellular functions. In the primary tumor, the highest scoring category was cancer. (FIG. 1)

A similar analysis of the cellular functions associated with the individual dysregulated genes from the liver metastasis is shown below (FIG. 2). Genes were highly enriched in cancer-related functions, similar to the primary tumor, but there was also much dysregulation of metabolic disease and lipid-metabolic genes. This may either represent some normal liver contamination of the sample, or less likely some hepatic-like differentiation of the metastatic tissue.

The network analysis yielded several findings of note. A first attempt was made to find pathways that were dysregulated in both the primary tumor and the liver metastasis in the hopes of targeting both sites. Following this strategy, two networks with similar features in both tumors were identified (FIGS. 3A and 3B). While there were differences in several individual genes in the two networks, commonalities included up-regulation of HIF1A, a transcription factor involved in tumor adaptation to, and survival in, hypoxic conditions. Also, in this network in both tumor sites, several members of the heat-shock protein (Hsp) family were present. Hsp family members have been appreciated as playing a role in tumorigenesis and maintenance (Xiang, T. X., et al., Tumori. (2008) 94:593-550, Yi, F., et al., ACS Chem. Biol. (2008) 46:181-191). Upon further examination of the individual Hsp family members up-regulated at the two sites, there was no individual member common to both sites that would be targeted by an approved, marketed drug, although there are investigational drugs against these targets.

However, it may be possible to indirectly target the Hsp pathways by inhibiting a family of proteins that interacts with them, the histone deacetylases (HDACs). HDACs were first identified as enzymes which deacetylated histone proteins, but more recently have been shown to have a wider array of substrates. One member, HDAC2 is up-regulated in the primary tumor and expressed in the liver metastasis. Other family members, including HDAC6 are expressed in both the primary tumor and the metastasis sample. It has been shown in the literature that HDAC inhibition is pro-apoptotic and has anti-tumor properties, through several mechanisms. One of these mechanisms is via hyperacetylation, and hence deactivation, of members of the Hsp family by HDAC6. In particular, Hsp90, which is up-regulated in both primary tumor and metastasis, has been shown to be deactivated by HDAC inhibition. FIG. 4, below, illustrates HDAC-Hsp interactions. There are several drugs inhibiting HDACs, including HDAC2 and HDAC6, such as vorinostat (Zolinza®).

An additional finding was that in the liver metastasis, the receptor tyrosine kinase, RET, and its surrounding pathway, were up-regulated (see FIG. 3B). RET is a proto-oncogene which is often up-regulated in endocrine tumors, however it has also recently been associated with breast cancers and neuroblastoma (Boulay, A., et al., Cancer Res. (2008) 68:3743-3751, Beaudry, P., et al., Mol. Cancer Ther. (2008) 7:418-424). Inhibiting RET would presumably selectively target the metastases only; however this may not be an issue as the primary tumor has been resected. Additionally, Sutent® is an inhibitor of RET.

The patient might be treated by inhibiting HDAC2 which should target both the primary tumor and the liver metastasis via deactivation of Hsp and other antitumor properties of HDAC inhibition. It is also possible to target liver metastases using sunitinib Sutent® to inhibit RET.

TABLE 1 Regulated Networks from Primary Colon Tumor Sample Focus ID Molecules in Network Score Molecules Top Functions 1 BHLHB2, COL1A2, ERK, GOPC, HSP90B1, IL6ST, 50 28 Cancer, Cellular ITGAV, LOX, LUM, LYZ, MRLC2, NEXN, Pak, Movement, Cell PDGF BB, POSTN, PSMA3, PSMD6, RAB1A, Morphology RAD23B, RHOA, Rock, ROCK1, S100P, SFRS10, SH3PXD2A, SKIL, SNRPF, SNRPG, SPARC, SUB1, TFIIH, Tgf beta, Ubiquitin, VCAN, VIM 2 ANXA2, C3-Cfb, CAPZA1, CD55, CFH, CFI, DAD1, 50 28 Neurological Disease, DDX5, EIF3E, EIF3H, FSH, G0S2, GJB2, GTF3A, Protein Synthesis, hCG, HSPH1, LDHA, LDHB, LDL, MCL1, NFkB, Connective Tissue Disorders PAIP2, PAWR, PLC, PTP4A1, RAB2A, RPL15, RPL39, RPS3A, S100A10, SERPINB5, TCP1, TPT1 (includes EG: 7178), UBE2K, Vegf 3 Actin, Akt, ATP2A2, Calmodulin, CALU, Caspase, 40 24 Cancer, Gastrointestinal CEACAM6 (includes EG: 4680), CLIC4, CSTB, DEK, Disease, Cell Signaling DSTN, Dynamin, ERH, HDAC2, HIF1A, HLA- DQA1, Hsp70, Hsp90, HSPA5, HSPA8, Jnk, MAP4K5, PAPOLA, PFN1, PI3K, PIK3C2A, PLOD2, Proteasome, RGS1, RNA polymerase II, S100A11 (includes EG: 6282), TCEB1, TMF1, TXNDC17, YWHAE 4 C15ORF15, D-glucose, EWSR1, FGA, Histone h3, 28 16 Carbohydrate Metabolism, HNF4A, HOOK3, KRR1, LAPTM4A, MRPL33, Molecular Transport, Small MRPS18C, N4BP2L2, PDE4DIP, SEC31A, SMAD4, Molecule Biochemistry SSBP1, TBC1D16, THYN1, TINP1, TM9SF2, USMG5, VPS29 5 ACTA2 (includes EG: 59), ALP, Ap1, Arp2/3, 27 18 Cell-To-Cell Signaling and ATP5E, BMPR2, C3ORF10, CALD1, CSNK1A1, Interaction, Cellular DCN, F Actin, FN1, GJA1, Histone h3, IFI16, IK, Assembly and Organization, IL12, Insulin, Interferon alpha, KIAA0265, Mapk, Cellular Movement Mmp, NCKAP1, Nfat, P38 MAPK, Pdgf, Pka, Pkc(s), PPIC, Rac, RAC1, S100A4, Scf, SPP1, TIMP2 6 ACTA2 (includes EG: 59), ANKH, BHLHB2, BIRC6, 27 18 Protein Degradation, CCNK, CGGBP1, CHD3, COMMD6, COMMD1 Protein Synthesis, Viral (includes EG: 150684), FMR1, H2AFZ, HIPK1, Function MAT2A, MCL1, NFIL3, NXF1, PDGF BB, PPA1, PRPF40A, PRRX2, RELA, RFFL, RNF25, SFRS10, SON, THOC7, TP53, TTC3, UBE2A, UBE2B, UBE2D2, UBE2D3, UBQLN2, UBR3, WBP5 7 ACTA2 (includes EG: 59), BET1, CCDC90B, 25 17 Cellular Assembly and CDK5RAP3 (includes EG: 80279), CISD1, COPB2, Organization, Cell COPZ1, COX7A2, CREBL1, CSDE1, HNF4A, Morphology, Connective HSPA4L, MCF2, MITD1, NFYB, PHF5A, PI4KB, Tissue Development and PRDX5, PRKAB1, RAB10, RAB11A, RTCD1, Function SCFD1, SF3B1, SF3B4, SF3B14, SYTL3, TAX1BP1, TBC1D17, TMEM93, TSNAX, UBE2D3, UFM1, USP5, YKT6 8 ATL3, ATP6V1B2, CLK4, CPNE1, CSNK1A1, 23 16 Cell-To-Cell Signaling and CTGF, DDX1, DHX15, EIF5B, FAP, GCC2, HAT1, Interaction, Cancer, Cellular HEBP2, ITGA1, ITGA3, ITGAE, KITLG (includes Growth and Proliferation EG: 4254), L-1-tyrosine, LOC100128060, MIA, MYCN, PDCD6, RELN, retinoic acid, RPL19, RPS19, RPS23, RPS17 (includes EG: 6218), SLC38A2, SNAI2, SPARC, TBC1D8, TGFBI, VEGFA, YWHAZ 9 BTF3 (includes EG: 689), C12ORF35, Ck2, DDX17, 21 15 RNA Post-Transcriptional DICER1, HMGN1, HNRNPA1, HNRNPA2B1, Modification, Cellular HNRNPC, LSM2, LSM3, LSM4, LSM5, LSM7, Assembly and Organization, LSM8, LSM6 (includes EG: 11157), MED21, Cellular Compromise NAP1L1, NONO, NOP5/NOP58, PICALM, PRPF8, RPL37, SAFB, SART3, SFRS3, SFRS12, SIP1, SMN2, SNRPA1, SNRPD1, SNRPD2, SNRPD3, SNRPE, SSB 10 BCAS1, beta-estradiol, BNIP3 (includes EG: 664), 21 15 Cancer, Cellular Growth CCNA1, CCNF, CDR1, CHMP2B, CKS1B, DYNLL1, and Proliferation, ERBB2, GLB1, HTRA1, hydrogen peroxide, IRF6, Reproductive System KRT81 (includes EG: 3887), LARP5, NUCKS1, Disease PIK3C2A, PKIA, PKIB, PRDX5, PRDX6, PRSS23, PTP4A2, RAB9A, S100A2, SKP1, SPARC, SSR1, SSR3, TERT, thiobarbituric acid reactive substances, WWC1, ZBTB7A, ZNF638 11 ANXA4, ASPN, CCL18, CCL19, CD40LG, 21 15 Cell-To-Cell Signaling and chondroitin sulfate, CNN3, CSDA, CTSH, CXCL2, Interaction, Hematological DDX5, EEA1, EGLN1, FSCN1, HLA-DQA1, IFNG, System Development and ITGB7, KDELR2, KIF2A, LOX, MARCKSL1, Function, Immune and METAP2 (includes EG: 10988), MMP11, MYC, Lymphatic System NAPG, NEDD9, NSF, PLS3, POMP, RPS15A, Development and Function SAMD9, SERINC3, TGFB1, WISP1, YME1L1 12 ARHGEF18, BRCA1, BTF3 (includes EG: 689), 21 15 Protein Synthesis, Post- CMTM6, CSDE1, CUL2, E3 RING, FSCN1, GH1, Translational Modification, MCC, MRPL36, NOVA1, NPEPPS, PDCD5, RHPN2, Cellular Assembly and RNF126, RPL35, SEC62, SEC63, SEC61A1, SEC61B, Organization SEC61G, SEPT2, SEPT7, SEPT9, SEPT11, SF3B14, TAGLN2, TBCA, TMCO1, TMED2, TRAF6, TXNDC1, UBE2D3, VHL 13 4-phenylbutyric acid, ANGPT2, ARNT2, ATIC, 17 13 Cell Signaling, Molecular C14ORF156, CD247, CD3E, CDV3, COL12A1, Transport, Vitamin and COL4A5, Cpla2, CUL2, CYBA, DEF6, EDIL3, Mineral Metabolism FCGR2C, GAB2, HNRPDL, KCNK3, LRRFIP1, MAT2A, NASP, OAZ1, SEPP1, SIRPA, SNRP70, spermine, TAX1BP1, TNF, TNFRSF9, TRA2A, TRAF6, UACA, YPEL5, ZAP70 14 AATF, ATN1, CCDC99, CDKN1A, CTCF, 15 12 Gene Expression, Cell CYP27B1, E2F1, E2F6, EFEMP1, EIF1, EPC1, Cycle, Connective Tissue FBLN2, FEN1, HIST2H2AA3, IL2, ITM2B, KRAS, Development and Function MEGF8, NAB1, NAB2, PCSK5, PTPRJ, PTRH2, REEP5, RERE, RPA3, RYBP, S100A4, Scf, SNF8, SP2, TFDP2, TSG101, VPS36, ZBED5 15 C7ORF43, TMEM50A 2 1 16 GPI, PARP14 2 1 Cancer, Cell Morphology, Cell-To-Cell Signaling and Interaction 17 ENY2, MCM3AP 2 1 Gene Expression, DNA Replication, Recombination, and Repair, Molecular Transport 18 C1GALT1, Glycoprotein-N-acetylgalactosamine 3- 2 1 Cardiovascular System beta-galactosyltransferase Development and Function, Cell-To-Cell Signaling and Interaction, Connective Tissue Development and Function 19 C21ORF66, GRIN1 2 1 Cardiovascular Disease, Cell Death, Cell-To-Cell Signaling and Interaction 20 DUB, USP34 2 1 21 DNAJC, DNAJC15, Hsp22/Hsp40/Hsp90 2 1 Carbohydrate Metabolism, Drug Metabolism, Molecular Transport 22 NADH2 dehydrogenase, NADH2 dehydrogenase 2 1 Cancer, Gastrointestinal (ubiquinone), NDUFC2 Disease

TABLE 2 Regulated Networks from Liver Metastasis Sample Focus ID Molecules in Network Score Molecules Top Functions 1 ABCA6, APCS, APOC1, APOC3, C3-Cfb, CFB, CFH, 47 29 Genetic Disorder, CFI, CP, CPB2, CTSL1, ERK, FG, FGA, FGB, FGG, Hematological Disease, Fibrin, FTL, GDI2, HP, HRG, HSBP1, Mmp, NAMPT, Ophthalmic Disease NTN4, QDPR, RAB1A, RAB8A, S100P, SAA4, SERPIND1, SSBP1, Stat3-Stat3, TFF2, TTR 2 ABCD3, AKR1C4, C14ORF156, Ck2, CREBL2, 45 27 Lipid Metabolism, Small DYNLL1, E2f, EIF5, EIF3H, EIF3J, FSH, Histone h3, Molecule Biochemistry, HLA-DQA1, HNRNPA1, HNRNPA2B1, HSPC152, Protein Synthesis IFITM2, IFITM3, IL12, Interferon alpha, ITM2B, KIAA0265, LDHA, MHC Class II, MT1G, NONO, PARP9, PGRMC1, PHYH, PTP4A1, RNA polymerase II, SC4MOL, SFRS5, SLC27A2, TFF1 3 APOA2, APOB, APOF, B2M, C5, C6, C7, C1q, C1R, 41 26 Hematological System C5-C6-C7, C5-C6-C7-C8, C8B, CALR, Complement Development and Function, component 1, CXCL16, DAD1, F5, F9, G0S2, HAMP, Organismal Functions, HDL, HSP90B1, IgG, LMAN1, MHC Class I, NFkB, Infectious Disease P4HB, PDIA3, PRDX4, PROS1, SAA@, SERPINC1, SERPING1, TFPI, WTAP 4 14-3-3, ATP2A2, CSTB, DNAJA1, ERH, HIF1A, 40 25 Post-Translational HSP, Hsp70, Hsp90, HSP90AA1, HSPA5, HSPA8, Modification, Protein IFN Beta, IL6ST, LYZ, Mapk, NSMCE1, PAPOLA, Folding, Cancer PFN1, PI3K, PPIA, PRDX6, Proteasome, PSMA3, PSMB2, PSMD6, RDX, RET, S100A11 (includes EG: 6282), SOD1, STAT, TCEB2, TEGT, Ubiquitin, YWHAE 5 ACADM, ACAT1, Akt, ALB, ALDOB, AMPK, 38 24 Energy Production, Nucleic ANGPTL3, APOH, ATP5E, ATP5J2, ATP5L, Acid Metabolism, Small COX5A, COX6C, COX7B, COX7C, CYB5A, Molecule Biochemistry Cytochrome c oxidase, H+-transporting two-sector ATPase, Hnf3, Insulin, LEPR, NADH dehydrogenase, NADH2 dehydrogenase, NADH2 dehydrogenase (ubiquinone), NDUFA1, NDUFA2, NDUFA4, NDUFAB1, NDUFB6, SCD, SLC2A2, TAT, Tcf 1/3/4, TMBIM4, Vegf 6 A2M, ADFP, ALDH1A1, BNIP3 (includes EG: 664), 33 22 Small Molecule CAR ligand-CAR-Retinoic acid-RXR&alpha, CAT, Biochemistry, Drug CD14, CYP2B6 (includes EG: 1555), CYP2C8, Metabolism, Endocrine CYP2C19, CYP2E1, CYP3A5, FABP1, GST, GSTO1, System Development and HSPE1, IGFBP1, Jnk, MGST2, MT1F, NADPH Function oxidase, Ncoa-Nr1i2-Rxra, Ncoa-Nr1i3-Rxra, P38 MAPK, PDGF BB, PXR ligand-PXR-Retinoic acid- RXR&alpha, Rxr, SAT1, SERPINA2, SNRPG, Trypsin, TXNDC17, UGT2B4, Unspecific monooxygenase, VitaminD3-VDR-RXR 7 C12ORF62, CEBPB, CWC15, HNF1A, HNF4A, 27 16 Gene Expression, Cellular HSDL2, LAPTM4A, MRP63, MRPL33, MRPL51, Development, Hepatic MRPS28, MT1X, N4BP2L2, ONECUT1, SEC11C, System Development and SLC38A4, TINP1, TM4SF4, TMEM123, TMEM176A Function 8 ALB, AOX1, ATXN2, BNIP3 (includes EG: 664), 24 17 Cellular Assembly and CYC1, CYTB, DAD1, FGF2, FTH1, Gsk3, hemin, Organization, Lipid iron, ITM2B, KRAS, LCP1, LRAT, MAPK9, MYCN, Metabolism, Molecular PLS3, retinoic acid, RPS7, SERPINA7, SLC38A2, Transport SPINK1 (includes EG: 6690), TMED2, Ubiquinol- cytochrome-c reductase, UQCR, UQCRB, UQCRC2, UQCRFS1, UQCRFSL1, UQCRH, UQCRQ, VHL 9 ANXA2, Ap1, ARHGEF12, Calpain, Cpla2, F2, F 23 17 Cell-To-Cell Signaling and Actin, Filamin, FN1, GJB2, hCG, IL1, LAMP2, LDL, Interaction, Cellular MT2A, PAH, Pak, Pdgf, Pkc(s), PLC, Pld, PP2A, Assembly and Organization, Rap1, Ras homolog, RHOA, Rock, S100A10, SAR1B, Cancer SMARCA1, SPP1, ST6GAL1, SUB1, Tgf beta, TIMP1, UBE2K 10 ACAA2, AIP, AKR1C4, BAAT, BET1, BRD4, 23 17 Lipid Metabolism, Small C6ORF203, CCDC45, CLDN1, CLDN3, FOXA2, Molecule Biochemistry, GJB1, GOT1, HAO1, HNF4A, HSD17B4, INADL Endocrine System Disorders (includes EG: 10207), LSM3, LSM4, LSM5, LSM10, MAL2, NR5A2, PBLD, SART3, SCFD1, SH3BGRL2, SHFM1, SNRPD2, SNRPD3, SNRPE, STRAP, TDO2, VPS29, YKT6 11 3-alpha,17-beta-androstanediol, 3-beta,17-beta- 23 17 Endocrine System androstanediol, ALB, beta-estradiol, C11ORF10, Development and Function, CDH5, CFHR4, cholesterol, COMMD6, CPS1, Small Molecule CYB5A, CYP3A4, DDR1, Gsk3, HSD17B6, IDH1, Biochemistry, Metabolic IL12B, IRS2, ITGAM, LEAP2, LEP, MAPK9, Disease MMP12, MYBL1, PCSK1, PIK3C2A, PON3, RELA, RPL36AL, SC4MOL, SC5DL, TBC1D8, TTPA, UGP2, VEGFA 12 AADAC, ACADSB, ACTA2 (includes EG: 59), 20 15 Cell-To-Cell Signaling and ADH4, ALB, AMBP, ANXA2, ASPN, BMP2, CDH11, Interaction, Skeletal and CHRDL2, CTNNB1, CTSS, CYC1, F13A1, FGF6, Muscular System GDF10, HNF1A, HRAS, ITGAE, KDELR2, LEO1, Development and Function, LGALS3, MAPK9, NR5A2, NUCB2, PCCA, PCSK6, Tissue Development phosphate, PZP, RPL37, RPS17 (includes EG: 6218), TBCA, TGFB1, TTC1 13 ACTA2 (includes EG: 59), AFM, ALB, APP, AUH, 20 15 Organismal Injury and CD4, CD40LG, CFHR5, Cu2+, CUGBP2, DECR1, Abnormalities, Tissue F12, GATM, hemin, heparin, HIST1H2BK, HPR, Morphology, Cell-To-Cell IFNG, IL4, IL13RA1, ITIH2, MAPK9, NFYB, POMP, Signaling and Interaction RNASE3, RRAGA, RRAGD, SEPP1, SERPINA5, SERPINA6, SON, TFCP2, TMEM93, TNFAIP2, UHRF1 14 ACTA2 (includes EG: 59), ALB, ARG1, ARRDC3, 18 14 Inflammatory Disease, butyric acid, CARHSP1, CCL18, CD4, CDH11, CTSF, Immunological Disease, DDR1, DDT, G0S2, GC, Gsk3, GTF3A, HLA-DRA, Viral Function IFNGR2, IK, IL15, IL18R1, MAPK9, MMP12, NNMT, NUP85, PAIP2, PIAS4, PRKRIR, PTP4A1, SYAP1, SYNPO, TNF, TNFAIP2, TP53, Vegf 15 Actin, C5, C6, C7, C8, C9, C21ORF66, C5-C6-C7-C8, 16 13 Cell Death, Hematological C5-C6-C7-C8-C9, C8A, C8B, C8G, Calmodulin, Disease, Organismal Injury CAMK2B, Caspase, CCL13, CD59, CIAO1, Cyp2b, and Abnormalities CYP2B7P1, Cytochrome c, dihydrotestosterone, FAM96A, GRIN1, ICAM1, IFNA1, IRS2, MYO1B, pyridine, RGN, RPS29, SEPP1 16 amino acids, ARL6IP1, ATM, CDC2L1 (includes 16 13 Cancer, Cell Death, EG: 984), CDC45L, CDKN1A, CHD2, CTCF, CTDP1, Hematological Disease CTSK, E2F1, EIF1, GLUD1, GNPNAT1, Gsk3, HGF, IL2, ITM2B, KIAA0999, LGALS3, MAP3K1, MAPK9, MYC, PTRH2, Ras, RPL38 (includes EG: 6169), RPS13 (includes EG: 6207), RPS15A, RPS27L, Scf, TGFA, TPT1 (includes EG: 7178), UBE2S, VCP, ZBED5 17 3-hydroxyisobutyryl-CoA hydrolase, HIBCH 2 1 18 ENY2, MCM3AP 2 1 Gene Expression, DNA Replication, Recombination, and Repair, Molecular Transport 19 PCSK6, PRG4 (includes EG: 10216) 2 1 Cancer, Cell Morphology, Cellular Development 20 BHMT2, TAL1 2 1 Cardiovascular System Development and Function, Embryonic Development, Hematological System Development and Function 21 IMP3, LPXN, MPHOSPH10 1 1 RNA Post-Transcriptional Modification, Post- Translational Modification, Protein Synthesis 22 E2F6, EPC1, REEP5 1 1 Gene Expression, Cellular Growth and Proliferation, Cancer 23 DNAJC, DNAJC15, Hsp22/Hsp40/Hsp90 1 1 Carbohydrate Metabolism, Drug Metabolism, Molecular Transport 24 CIC, RHOJ, SEC62, SEC63 1 1 Cancer, Hepatic System Disease, Protein Synthesis

ADDITIONAL REFERENCES

-   Mariadason, J. M., et al., Epigenetics (2008) 3:28-37. HDACs and     HDAC inhibitors in colon cancer. -   Yang, R, et al., Cancer Res. (2008) 68:4833-4842. Role of     acetylation and extracellular location of heat shock protein 90alpha     in tumor cell invasion.

EXAMPLE 2 Formulation of Treatment for Patient with Lung Cancer

Biopsy samples from the patient's tumor and normal tissue were assayed for mRNA levels using Affymetrix transcription profiling. Genes with an expression ratio threshold of 3-fold up- or down-regulation, and a significance P-value of 0.05 yielded 4,519 unique genes.

Using a tool provided by Ingenuity Systems, the 4,519 genes were subjected to an algorithm which finds highly interconnected networks of interacting genes (and their corresponding proteins). Protein/protein interaction is determined directly from the research literature and is incorporated into the algorithm. These findings were then further analyzed to find particularly relevant pathways which could provide potential therapeutic targets or, if possible, clusters of interacting proteins which potentially could be targeted in combination for therapeutic benefit.

An initial analysis was done to confirm that the global gene expression from the tumor sample was generally consistent with a priori expectations for a neoplasm of this type. This serves as a crude measure of quality control for tissue handling and microarray processing methodology. The networks that were assembled by the protein interaction algorithm from the filtered list of up- or down-regulated genes were analyzed with respect to the cellular and organismal functions of their individual component genes. The three top-scoring networks (with respect to the interconnectedness of their component genes) were greatly enriched in the following functions:

Cancer Cellular Assembly and Organization Cellular Function and RNA Post-Transcriptional Modification Maintenance Embryonic Development Cell Cycle

This overall pattern is consistent with what one might expect from the global gene expression of a tumor sample, as compared to normal tissue, and help to confirm that the data are from the tumor sample and that the integrity of the gene expression milieu has been maintained. The entire list of networks and their associated functions is set forth at the end of this example.

The differentially expressed genes were also scored for associated cellular functions. The highest scoring category was Cancer. (FIG. 5) Note also the high scoring of cell proliferative gene functions: Cell Cycle, Cellular Growth and Proliferation, Gene Expression.

Network analysis of the Affymetrix data revealed up-regulation of many components of the PDGF pathway. Most notably, the receptor PDGFRα and two of its ligands, PDGFα and PDGFC were over-expressed. Notably, several downstream effectors of PDGFα/PDGFRα, specifically, STAT3 and PI3K, are also up-regulated, indicating dysregulation of this signaling pathway that is implicated in carcinogenesis (Andrae, J., et al., Genes & Dev. (2008) 22:1276). Increased PDGF signaling has been observed in several neoplastic conditions (Dai, C., et al., Genes & Dev. (2001) 15:1913-1925; Smith, J. S., et al., J. Neuropathol. Exp. Neurol. (2000) 59:495-503; Arai, H., supra; Zhao, J., et al., Genes Chromosomes Cancer (2002) 34:48-57). This may represent an attractive intervention target, as inhibition of the tyrosine kinase activity could dampen downstream activity in the pathway and possibly lessen the stimulatory effects of the pathway on cell proliferation and survival. See FIG. 6.

The tyrosine kinase domain of PDGFRα is inhibited by imatinib (Gleevec®) and by sunitinib (Sutent®), which also targets VEGF. While the primary target of imatinib is the receptor tyrosine kinase c-ABL, it is also known to act at other targets including c-KIT and PDGFR (e.g., Wolf, D., et al., Curr. Cancer Drug Targets (2007) 7:251-258). Thus, regardless of the mutational status of c-KIT or c-ABL in this tumor, inhibition of PDGFRα may be considered by virtue of its potentially inhibitory effects on the up-regulated PDGF pathway.

The potential cellular mechanisms by which the PDGF pathway may promote tumor growth are manifold. As shown in FIG. 7, below, PDGF may act directly on tumor cells in an autocrine or paracrine manner to enhance proliferation, or may act indirectly via recruited fibroblasts and pericytes to influence angiogenesis and/or invasion and metastasis. (Andrae, J., et al., Genes & Dev. (2008) 22:1276.)

Also of note, VEGF was highly up-regulated (111-fold). To a lesser extent, its receptor and downstream effectors were up-regulated as well (FIG. 8). While this finding may have already been addressed by previous antiangiogenic therapy that is exhibiting response, it is significant to discuss here, as Sutent® has activity against VEGF in addition to inhibiting PDGFR. If weighing the relative merits of Gleevec® versus Sutent®, this may become relevant.

Our network analysis revealed that the PDGF pathway is strongly activated in the patient's samples, and has been shown in the literature to activate several mechanisms which directly and indirectly support tumor biology. Furthermore, there are FDA-approved therapies known to impact the PDGF pathway: imatinib (Gleevec®) and sunitinib (Sutent®).

Since there are negative data, algorithm 1b is applied to take account of two data points that are inconsistent with the hypothesis, the calculated probability of the pattern being produced only by chance (Π) is 4×10⁻⁷.

In this case, the total pathway elements (q)=15, which are 4 ligands (PDGF α, β, C, and D); 2 receptors (PDGFR α, β; 2 receptor inhibitors (Oav 1/3, GRB 2); 5 intermediaries before STAT (PKR, JAK1, JAK2, JAK3, SRC); and 2 STATs (STAT1, STAT3).

The total aberrant genes consistent with hypothesis (n) is 10, which are 2 ligands (PDGF α and C); 1 receptor (PDGFR α); 5 intermediaries before STAT (PKR, JAK1, JAK2, JAK3, SRC); and 2 STATs (STAT1, STAT3). The total aberrant genes inconsistent with the hypothesis (n′) is 2: 2 receptor inhibitors (Oav 1/3, GRB 2). The total number of possible pathways (N) is 283 canonical pathways in Ingenuity, the cut-off probability (p) is 0.05.

Then Π₊=2×10⁻¹⁰, Π⁻=0.2 (these represent the “raw” probabilities of the hypothesis being false and the reverse hypothesis being false, respectively), leading to Π′=1×10⁻⁹ and further to Π=4×10⁻⁷ after the multiple pathway correction.

In algorithm 2b, inputting the specific p-values associated with the positive genes (ranging from 0.04 to 5×10⁻⁴) and those for the negative genes (0.01 and 1×10⁻⁴) leads to: Π₊=2×10⁻¹⁹, Π⁻=7×10⁻⁵, Π′=4×10⁻¹⁵ and further to Π=1×10⁻¹² after the multiple pathway correction that probability of pattern being produced by chance (Π) is 1×10⁻¹².

Using algorithm 3b, the privileged pathway elements (μ) is 6 which are 4 ligands, 2 receptors. The consistent aberrant privileged genes (τ) is 3, which are 2 ligands, 1 receptor. The inconsistent aberrant privileged genes (τ′) is 0; non-privileged pathway elements (q) is 9; consistent aberrant non-privileged genes (n) is 7; and inconsistent aberrant non-privileged genes (n′) is 2.

Inputting these values gives a probability of pattern being produced by chance ranges depending on the value of β from 6×10⁻⁵ for β=1 to 1×10⁻¹² for β=0.

Our results showed that while EGFR was upregulated, there did not appear to be any activity in the rest of its pathways, so it was concluded that this upregulation was not clinically significant—i.e., that regardless of its upregulated state, it did not appear to be an important driver of malignancy in this tumor. It was then learned that the patient had been previously treated by Tarceva in response to a (positive) test for mutations in EGFR, and had shown no response on this therapy. Subsequent administration of Avastin as part of “trial and error” did show a partial response—Avastin targets VEGF. Our results indicated VEGF as a target.

As there are 6 aberrant genes inconsistent with the VEGF pathway, algorithm 1b is applied, and it yields a probability of pattern being produced by chance (Π) is 2×10⁻¹⁰.

In this case, the total pathway elements (q) is 47, which are 2 ligands (VEGF A, B); 2 receptors (KDR, FLT-1, both in the VEGFR family); 12 PI3K members (Cα, Cβ, Cγ, Cδ, C2α, C2β, C2γ, C3, R1, R2, R3, R5); 2 PLCγ forms (PLCγ1, PLCγ2); 3 AKT forms (AKT1, AKT2, AKT3); 2 PKC forms (PKCα, PKCβ); 6 additional in survival branch (14-3-3σ, 14-3-3ε, FKHR, eNOS, BAD, Bcl XL, Bcl 2); 2 SOS forms (SOS 1, SOS2); 6 RAS forms (HRAS, KRAS, MRAS, NRAS, RRAS, RRAS2); 2 MEK forms (MEK1, MEK2); 5 ERK forms (MAPK1, MAPK3, MAPK6, MAPK7, MAPK12); and 3 additional in proliferative branch (SHC, GRB2, c-Raf).

The total aberrant genes consistent with hypothesis (n) is 20, which are 1 ligand (VEGF A); 1 receptor (KDR); 5 PI3K members (Cα, Cβ, C2α, C3, R1); 1 PLCγ form (PLCγ2); 3 AKT forms (AKT1, AKT2, AKT3); 1 PKC form (PKCα); 1 additional in survival branch (14-3-3ε); 1 SOS form (SOS1); 1 RAS form (KRAS); 1 MEK form (MEK1); 2 ERK forms (MAPK1, MAPK7); and 2 additional in proliferative branch (SHC, GRB2).

The total number of aberrant genes inconsistent with the hypothesis (n′) is 6 which are 3 additional in survival branch (14-3-3σ, FKHR, Bcl XL); 1 SOS form (SOS2); 1 RAS form (RRAS2); and 1 MEK form (MEK2).

The total number of possible pathways (N) is 283 and the cut-off probability (p) is 0.05. Then Π₊=2×10⁻¹⁴, Π⁻=0.03 (these represent the “raw” probabilities of the hypothesis being true and the reverse hypothesis being true, respectively), leading to Π′=9×10⁻¹³ and further to Π=2×10⁻¹⁰ after the multiple pathway correction.

Inputting the specific p-values associated with the positive genes (ranging from 0.04 to 1×10⁻⁴) and those for the negative genes (ranging from 0.05 to 0.005) into algorithm 2b leads to: Π₊=3×10⁻³³, Π⁻=5×10⁻⁵, Π′=7×10⁻²⁹ and further to Π=2×10⁻²⁶ after the multiple pathway correction, giving a probability of pattern being produced by chance (Π) is 2×10⁻²⁶.

Applying algorithm 3b, the privileged pathway elements (μ) is 4, which are 2 ligands+2 receptors.

The consistent aberrant privileged genes (τ) is 2; inconsistent aberrant privileged genes (τ′) is 0; non-privileged pathway elements (q) is 43; consistent aberrant non-privileged genes (n) is 18; and inconsistent aberrant privileged genes (n′) is 6.

This provides a probability of the pattern being produced by chance, depending on the parameter β, ranging from 8×10⁻⁶ for β=1 to 2×10⁻²⁶ for β=0.

TABLE 3 Regulated Networks from Lung Cancer Sample Focus ID Molecules in Network Score Molecules Top Functions 1 ALCAM, ARL6IP5, ATP2A2, CANX, CAV1, CAV2, 25 35 Cancer, Cellular Assembly CD59, CEP170, CLINT1, CLTA, DOM3Z, EMP2, and Organization, Cellular EPS8, FLOT1, FLOT2, GJA3, GNA11, HCCS, Function and Maintenance KPNB1, LIN7C, LRRFIP2, NGFRAP1, PCMT1, RBM4, SH3BGRL2, SLC1A1, SQRDL (includes EG: 58472), SVIL, TACSTD1, TCEB2, TENC1, TMOD3, WFS1, WSB1, XPNPEP3 2 ARHGAP18, ARMC8, BCL2L1, BECN1, BFAR, 25 35 RNA Post-Transcriptional CLCN3, CLK1, CSPG5, DYNC1I2, DYNLRB1, Modification, Cellular DYNLT3, FGD2, FIP1L1, GANAB, GOPC, GRID1, Function and Maintenance, HTRA2, LUC7L, MKLN1, MPHOSPH6, NKTR, Embryonic Development RNPS1, SFRS2, SFRS4, SFRS6, SFRS8, SFRS11, SFRS2IP, SRPK1, SRPK2, STK17B, VDAC1, ZFP91, ZFR, ZRSR2 3 BTG3, CDK2, CDK2AP1, CIRBP, CNBP, CNOT1, 25 35 Cancer, Cell Cycle, CNOT2, CNOT3, CNOT6, CNOT7, CNOT8, Skeletal and Muscular CNOT6L, ERH, LATS1, LXN, MOBKL1B, MRPS14, Disorders PABPC1, PAIP1, PAN3, PINX1, PPM1A, PSMF1, PSPH, RBM7, RBMX, RNF126, RPL23A, RPL35A, RPS16, RPS28, RQCD1, STK38, TAGLN2, TBCA 4 ABCC4, ALG3, ASXL1, ASXL2, ASXL3, CDK10, 25 35 Drug Metabolism, CKS2 (includes EG: 1164), CNN3, CSRP1, CSTF3, Molecular Transport, DDX19A, DYNC1LI2, EBP, EIF4A2, EIF4G3, Nucleic Acid Metabolism FAM134C, G0S2, GLTSCR2, HCN2, IDI1, IFRD1, MIF4GD, PDCD4, PHF19, PINK1, PMS2L3, PTEN, RNASEH1, RPL22, RPL39, RPS13 (includes EG: 6207), SEL1L, SHFM1 (includes EG: 7979), TRAM1, TRAPPC3 5 ATF7, C4ORF34, C6ORF203, CLNS1A, CSDE1, 25 35 RNA Post-Transcriptional EBF1, FBXL11, FLJ20254, GEMIN7, GPRC5C, Modification, Cellular ITSN2, KCTD3, LSM1, LSM4, LSM5, LSM8, Development, Connective MAP2K1, NARS, OLA1, PCDH9, PLEKHA5, Tissue Development and PPAP2B, PRIC285, PRMT5, SNRPD2, SNRPD3, Function SNRPE, SNRPG, SPTLC2 (includes EG: 9517), ST7, STRAP, TBL1XR1, THUMPD3, TRAF3IP3, ZNF706 6 BRD2, BRP44, C11ORF61, C20ORF24, CIDEB, 25 35 Cancer, Immunological CRBN, DFFA, DMXL2, DPM1, EFTUD2, GCN1L1, Disease, Lipid Metabolism HEATR1, KIAA0406, KIAA1967, MED1, MLSTD2, MRPL41, NUP54, NUP188, OPA1, PDXDC1, PRKD3, PTP4A3, RAB3GAP1, SATB1, SSR1, SSR3, STRN4, TMEM33, TMEM41B, TOMM22, TOMM70A, UBE4A, USMG5, ZNF294 7 ADNP, ARID2, ARID1A, ARID1B, ATG7, ATG10, 25 35 Gene Expression, Cellular ATG12, C19ORF2, C20ORF67, CCNK, CEP76, Assembly and COL14A1, DHFR, DIDO1, EPB41L5, HELZ, Organization, Cellular HTATSF1, KCNRG, KIAA1279, MLLT10, PAICS, Compromise POLR2A, SF1, SF3A1, SF3A2, SF3B1, SF3B3, SMARCA2, SMARCB1, SMARCC1, SRGAP3, SUPT6H, TCERG1, WWP2, ZNF638 8 AFF4, AHCTF1, CCAR1, CDC2L6, CHD9, CROP, 25 35 Gene Expression, Cell DDX50, DDX52, DIMT1L, EIF4A1, ELL2, GCOM1, Signaling, Cell Cycle IKZF5, KIAA1310, KPNA4, MED8, MED13, MED16, MED19, MED23, MED24, MED25, MED26, MED13L, MED7 (includes EG: 9443), NOP5/NOP58, POLR2J, POLR2K, PUM2, RBM39, RECQL, TARDBP, WDTC1, ZCCHC10, ZNF281 9 AFTPH, ALOX5, AP1G1, AP1S2, AP1S3, 25 35 Hematological System C11ORF31, C4ORF42, CFB, CPSF3L, CREG1, Development and Function, DALRD3, DERL1, ERLIN2, ETF1, FAM14A, Tissue Morphology, Cancer GNPNAT1, HBB (includes EG: 3043), HBG2, HBZ, IGF2R, KITLG (includes EG: 4254), KLHL24, MID1IP1, PGLS, PLSCR1, PLSCR3, RANBP2, SECISBP2, SELT, SEPHS2, SPG7, TXNIP, VCP, YIPF3, ZNF207 10 ANKRD36B, ASMTL, C11ORF58, C14ORF147, 25 35 Cellular Movement, CCL11, CDIPT, DPY19L1, FABP5, FKBP3, Hematological System FLJ11184, GLT8D3, JARID1B, KCTD12, KIAA0152, Development and Function, KLF10, LIMCH1, MCC, MT1X, MYH10, NKX3-2, Immune Response NSMCE4A, OSM, PAX9, PPIA, PRDX3, PRDX6, PSD3, PSIP1, PUS7, RIPK2, RPL38 (includes EG: 6169), S100A8, SMC5, TDO2, TXNDC1 11 ADM, BAT2D1, BTN2A1, CALCRL, CCR1, CD209, 25 35 Cancer, Cell-To-Cell CEL, CLIC2, COL12A1, CUGBP2, CYBB, EDNRA, Signaling and Interaction, GPR34, GTPBP1, HIGD1A, HOXA9, IRX3 (includes Skeletal and Muscular EG: 79191), ITGAM, LOC339047, LY86, MSI2, Disorders NAALAD2, NANOG, NAP1L4, NPIP, NSUN5C, OAZ1, OSBPL8, OTP, PHACTR2, RAB31, RAMP2, SIM1, SRP72, TNC 12 ABCD3, API5, ASAH1, BCAP29, C1ORF142, 25 35 Genetic Disorder, Lipid COX4NB, DC2, FADS1, GMPPB, HMGB3, KCNH7, Metabolism, Metabolic MAGED1, MLF2, MTHFD1L, NOVA1, PGRMC1, Disease PJA2, PRC1, RAB23, RGS1, RPE, SFXN1, SFXN4, SRPRB, TOR1AIP2, TRIM37, TSC22D1, TSC22D4, TTC35, UBLCP1, UQCC, YY1, ZC3H7A, ZMYND10, ZNF580 13 ATE1, Catalase, CBWD1, CMTM3, CNIH4, CSK, 23 34 Cell Morphology, Cellular DUSP16, DUSP22, EFS, ILK, IMP3, ITGA9 (includes Development, Amino Acid EG: 3680), KLF11, LIMS1, LPXN, MAPK1, METAP1, Metabolism OTUD4, PARVA, PARVB, PHF21A, PTPN12, PTPN18, PTPN22, RGS5, RSU1, SCLT1, SEMA3F, SLC4A1, ST5, STYK1, TEX10, TGFB1I1, TPR, ZHX2 14 AIFM2, ANLN, ASPM, C14ORF106, DR4/5, EGFL6, 23 34 Cancer, Cell Cycle, FAM3C, GLIPR1, HMGN2, IKIP, JMJD1C, Genetic Disorder LETMD1, MBNL2, MORC3, MPV17L, MTDH, PPP4R2, PQLC3, PRODH (includes EG: 5625), RECQL4, RFFL, SCO2 (includes EG: 9997), SLC19A2, SMA4, SMG1, SNRK, STEAP3, TMED7, TMEM97, TP53, TPRKB, TULP4, UBL3, ZMAT3, ZNF84 15 ATIC, ATYPICAL PROTEIN KINASE C, BHLHB2, 23 34 Cell Morphology, Nervous BHLHB3, C17ORF42, CLN6, CMBL, COX5B, DVL3, System Development and ECT2, EME1, FFAR2, HNRPDL, IL29, IL10RB, Function, Cancer IL13RA1, IL27RA, IL9R, KIF3A, KIF3B, LMO4, LMO1 (includes EG: 4004), MIRN21 (includes EG: 406991), PARD3, PARD6G, PITPNB, PRKCI, PTCRA, RAB2A, SOCS7, SPCS2, STAT3, STMN3, TMF1, ZNF148 16 AKT1, COL4A2, FBLN1, HEYL, HIPK3, KLF15, 23 34 RNA Post-Transcriptional LIMD1, MATR3, METTL1, MORC4, MTCP1, Modification, Cell MTMR4, MUT, NDRG2, NEXN, NID1, PKC Signaling, Post- ALPHA/BETA, PLAC8, PLXDC1, POP4, PPHLN1, Translational Modification PPL, PSG3, RICTOR, RPP14, RPP25, SKIL, SVEP1, THAP5, TRIB2, TSC1, TSKU, TTF2, UBE4B, ZNF107 17 ABI2, APBB1IP, ARF5, B4GALNT1, CCDC53, 23 34 Cardiovascular System DGKA, DMN, ENAH, GART, GTF3A, GTF3C1, Development and Function, GTF3C2, HIF3A, HTATIP2, KIAA0368, NCOA5, Embryonic Development, NRP1, NRP2, OPTN, PCM1, PLXNA1, PLXND1, Organismal Development RAB11B, RAB11FIP3, RAB11FIP4, Sema3, SEMA3B, SEMA3C, SEMA6D, SNAI2, STC1, TBC1D8, TMSB10, VASH1, VEGFA 18 AMOTL2, CASC3, DCP2, DCP1A, DCP1B, DDX6, 23 34 RNA Damage and Repair, DEK, EIF2C1, EIF2C2, EXOSC6, G3BP2, Hdac1/2, Viral Function, Cell Cycle HIST1H3A, ING4, ITGB1BP1, KRIT1, MAGOHB, PDS5A, RAD21, RBM8A, RELA, SKIV2L2, SMC3, SMC1A, SRRM1, SRRM2, STAG1, STAG2, SYCP3, UPF2, UPF3A, UPF3B, WAPAL, XRN1, ZDHHC8 19 CHI3L1, COL16A1, COMMD1, COMMD2, 23 34 Gene Expression, Cellular COMMD3, COMMD10, DAB2, ELF1, ELF2, ELF4, Development, ELK3, ETS, ETS2, FLT1, HIVEP2, IPO8, KARS, Hematological System KPNA1, KPNA3, LMO2, LSM12, NFKB1, NFKBIL2, Development and Function NNMT, NUP50, RIPK5, SLC25A6, SLC39A14, SRP19, SRP54, TRIM3, TRIP4, TUBB6, UBA3, UBE2K 20 AMACR, ARL15, C16ORF14, CCDC90B, DBI, 23 34 Genetic Disorder, EIF1AD, FAM101B, FAM123A, FUNDC2 (includes Metabolic Disease, Cellular EG: 65991), LAMA4, NACA, PCTK1, PEX3, PEX5, Assembly and Organization PEX10, PEX13, PEX14, PEX19, PEX26, PEX11B, PMP22, RBM17, RNF135, SAT1, SCP2, SERPINB9, SLC25A17, Soat, SOAT1, STAT1, TFG, TNFRSF10C, TNRC6A, UBR1, ZHX1 21 AZIN1, BDP1, BNC2, BRD8, C20ORF20, CDK4- 23 34 Small Molecule Cyclin D2, EAF1, EID1, EP400, EPC1, EPC2, Biochemistry, Cancer, FLJ20309, GMPS, H1F0, ING3, INOC1, JARID1A, Skeletal and Muscular LACTB, LIN37, MAGEF1, MORF4L1, MORF4L2, Disorders MRFAP1, OAZ2, PHF12, PLSCR4, RB1, RBBP6 (includes EG: 5930), REEP5, RSL1D1, RUVBL2, SRPR, THOC1, THOC2, TRIM27 22 Ahr-aryl hydrocarbon-Arnt-Esr1, ATN1, BICC1, 23 34 DNA Replication, C9ORF86, COL15A1, CSNK1G2, CUEDC2, Recombination, and CXORF45, DAZ4, DECR1, DNTTIP2, DZIP1, Repair, Cellular EFEMP1, ESR1, FAM103A1, GFI1B, HAT1, HYPK, Development, Cellular KIAA0182, LCOR, LENG8, MYST3, NELL1, PNRC2 Growth and Proliferation (includes EG: 55629), QKI (includes EG: 9444), RBM9, RBPMS, RCHY1, RERE, RNF138, SLC39A8, SLIT1, TM4SF1, TRIM16, TRIM22 23 ASCL2, BCLAF1, C16ORF53, CCDC123, 23 34 Cellular Assembly and CDC42EP3, CEBPB, CPB2, DPY30, GMCL1, Organization, Cellular Histone-lysine N-methyltransferase, LEMD3, MAP4, Compromise, Protein MLL2, MLL3, NCOA6, NFKBIZ, NSD1, PRPF3, Synthesis PSPC1, RAB10, RBM14, SART3, SEPT2, SEPT6, SEPT7, SEPT8, SEPT9, SEPT11, SETD7, TFDP2, TMEM176A, TPT1 (includes EG: 7178), TUBB, UTX, WHSC1L1 24 AKAP2, BTF3 (includes EG: 689), C9ORF80, CCL18, 23 34 Cancer, Genetic Disorder, CREBL2, ECM2, EMR2, EXT1, EXT2, FBXO9, Nucleic Acid Metabolism FOXN3, HIG2, HYOU1, IRS1, KIAA0999, KLF12, LYK5, MARK3, MARK4, MRPS10, MRPS15, NOL11, NUAK1, PMPCB, PRKRA, PRPS1, PRPSAP1, PRPSAP2, Ribose-phosphate diphosphokinase, RPS29, SAAL1, SNF1LK2, SPARCL1, STK11, TUBE1 25 ATXN3, CDC25B, CHD4, CLOCK, CREBBP, CRY1, 23 34 Gene Expression, CUX1, ERG, ETV1, EYA3, FOXM1, H3F3A, H3F3B, Reproductive System HMGN1, MAP3K7IP3, MBD1, NAPSA, NCOA1, Development and Function, NCOA2, NCOA3, NKX2-1, NPAS2, Pias, PIAS1, Cell Morphology PIAS2, PIAS3, PLAGL1, RBCK1, SFTPB, SMARCE1, TACC2, TAGLN, TBX19, TP53BP2, TROVE2 26 ABCA1, ACTC1, ACTG1, CADPS2, CDC2L5, 23 34 Developmental Disorder, CENPF, DGKZ, DMD, DSTN, DTNB, EGLN1, Genetic Disorder, Skeletal EIF4B, ELP2, GAK, IFN ALPHA RECEPTOR, and Muscular Disorders IL6ST, JAK2, MAPK8IP3, MEP1B, MTIF2, NCAPG (includes EG: 64151), NPM1 (includes EG: 4869), OS- 9, OSMR, PDLIM5, PTPRK, RASGRP1, SBDS, SCN4A, SNTB2, SOCS5, SYNE2, TMSB4Y, UBASH3B, UTRN 27 ANKRD44, DDX56, DUB, HECW1, MARCH7, 23 34 Post-Translational MED20, UCHL1, UCHL3, UCHL5, USP1, USP3, Modification, Behavior, USP4, USP6, USP7, USP10, USP12, USP14, USP15, Cellular Function and USP18, USP28, USP31, USP32, USP33, USP34, Maintenance USP37, USP42, USP45, USP46, USP47, USP48, USP53, USP54, USP9X, USP9Y, ZNF423 28 ABHD2, AGR2, ARL4C, ARRDC3, ATP1A3, 23 34 Molecular Transport, ATP1B3, ATP1B4, C19ORF12, CCNE2 (includes Cancer, Cell Death EG: 9134), CDH11, CLEC2B, CTSH, CUTC, DYNC1H1, FGF14, FXYD2, GMNN, HNRPUL1, HPGD, KCNJ2, LGALS3, MFGE8, MGC16121, Na-k- atpase, NADSYN1, PBRM1, PDLIM3, PGM2L1, PLEK2, PRICKLE1, REST, S100A2, SCHIP1, SMARCA4, TBX2 29 ANAPC5, BAT1, DICER1, DNA-directed RNA 23 34 RNA Post-Transcriptional polymerase, DNM2, EXOC4, HNRPLL, HNRPM, Modification, Gene HSPC152, MED31, MGC13098, MSH6, NCAPH2, Expression, Organ NOLC1, NONO, ORAI2 (includes EG: 80228), PNN, Morphology POLR1D, POLR2C, POLR3C, POLR3F, POLR3H, PPIG, PRPF4B, PTBP1, RAVER2, RBM4B, RPL37, SFRS3, SFRS18, SMC4, SSB, STC2, TMPO, ZCCHC17 30 ARIH1, BAG4, C11ORF9, CHORDC1, CKAP4, 23 34 Post-Translational CTNNA1, DNAJB11, DOK3, EIF4E2, FANCC, FHL2, Modification, Protein HEPH, HFE, HNRPK, HSP90AA1, HSPA8, HSPA1A, Folding, Cancer HSPA1B, IFI44L, JINK1/2, LRRC59, PHLDA1, PSPN, RET, RNF19A, S100A11 (includes EG: 6282), SFN, ST13, SYPL1, TBC1D9, TFRC, TXN, XPOT, YWHAE, YWHAQ (includes EG: 10971) 31 ATXN1, C14ORF139, C1ORF65, C20ORF77, 23 34 Lipid Metabolism, Small C6ORF199, CADPS, CALCOCO2, DBNDD2, DZIP3, Molecule Biochemistry, FAS, FBF1, FBXL18, FLJ10404, GABARAPL2, Molecular Transport GBAS, LRSAM1, LUC7L2, METT11D1, MGAT4B, MTERFD2, NASP, NOL3, PEA15, PHPT1, PUM1, R3HDM1, SFRS14, SGMS1, SSFA2, TBC1D5, TNFR/Fas, TTC19, TUBGCP2, TUBGCP4, UBAP2L 32 ARL5B, C12ORF35, C16ORF57, CAMTA1, 23 34 Cancer, Cell Cycle, Tissue CCDC59, CROT, CTSB, DEDD, EEF1A1, GFM1, Morphology GPNMB, HSPE1, KIAA1712, KIF1B, NUDT3, PAPSS1, PAPSS2, PHYH, Protein-synthesizing GTPase, PRSS1 (includes EG: 5644), RAB2B, RANBP3, RNF167, RPLP1, RSRC1, S100A10, SCN11A, SCN5A, SLC25A13, SMAD4, SOX30, THRAP3, TMPRSS3, TRIM33, WARS 33 CAPZA1, CAPZB, CCBL2, CCNA2, CDS2, 23 34 Dermatological Diseases CGGBP1, Cyclin A, EMX2OS (includes EG: 196047), and Conditions, Genetic FAM19A2, FAM49B, FLCN, FMR1, FXR1, Disorder, Nervous System HIST1H2BC, KLHL3 (includes EG: 26249), METTL9, Development and Function MLL5, MSRA, MTPN, NAALADL2, NFYB, NPTX2, PITPNM2, RABIF, RIMBP2, SLC25A28, SLMAP, ST8SIA1, TBC1D22A, TM7SF3, TMEM87A, TTC3, VPS52, ZMPSTE24, ZMYM3 34 ADAMTS9, AIM2, B3GNT5, BEX1 (includes 21 33 Amino Acid Metabolism, EG: 55859), CARD14, CBR3, CD14/TLR4/LY96, Post-Translational CHM, CXCL16, ECOP, FNTA, FNTB, HDGF, Modification, Small IRAK1BP1, LY96, MFHAS1, NFkB, NKIRAS1 Molecule Biochemistry (includes EG: 28512), NOL14, PAK1IP1, PNKD, PTP4A2, RAB7A, RABGGTB, RAP2A, RASSF4, RNF19B, SLC11A2, STK10, SUMO4, TNFAIP8, TNFSF18, TRIM69, UNC5CL, WTAP 35 Akt, AKTIP, ARMCX3, ARS2, ASAH2, BRD7, 21 33 Lipid Metabolism, Small C11ORF79, C1ORF103, C1ORF174, CEACAM6 Molecule Biochemistry, (includes EG: 4680), DEXI, EIF3J, FKHR, GOLM1, Post-Translational IMMT, KBTBD7, KIAA1377, MAL2, MCAM, Modification MRPL44, NIPSNAP3A, PCTK2, PHIP, PPT1, RER1, RPL13A, SEC14L2, SLC40A1, SOCS4, TACC1, TPD52, TPD52L1, TXNDC9, UXS1, ZFAND3 36 ADI1, ANP32A, ANP32B, APEX1, CDKN1A, 21 33 RNA Post-Transcriptional DDX17, DLG5, DSE, ELAVL1, ERO1L, FBXO38, Modification, DNA FEN1, Foxo, HMGB2, HNRNPA2B1, KIAA0101, Replication, KIF20A, KLF7, KPNA6, NPAT, Oxidoreductase, Recombination, and SEPW1, SET, SETBP1, SFPQ, SFRS1, SFRS12, Repair, Gene Expression SLC30A5, SNRPA1, TOPORS, TTLL5, VEZT, VGLL4, WHSC2, WWOX 37 AP1GBP1, APBB2, BICD2, BZW1, BZW2, DEGS1, 21 33 Lipid Metabolism, Small EDEM1, EGFR, Egfr dimer, EML4, GAS5, KDELR1, Molecule Biochemistry, KDELR2, LRRFIP1, MAN2B1, Mannosidase Alpha, Cancer MTM1, MTMR2, MTMR12, NEK6, NEK7, NEK9, RPS25, RUSC1, SBF2, SCAMP1, SCAMP2, SERINC3, SGSM2, SH3BGRL, SLC16A1, SNX13, SURF4, TNXB, VDAC3 38 ACTR1A, ANKHD1, ATP7B, BICD1, C14ORF166, 21 33 Cell Cycle, Embryonic CDC42SE1, CEP63, CHML, CLASP2, CLIP1, Development, Hair and CRK/CRKL, DCTN2, DCTN4, DIAPH3, DISC1, DST, Skin Development and Dynein, ERN1 (includes EG: 2081), EXOC1, GLRX, Function MAPK8, MAPRE1, MDFIC, NDEL1, NIN, PAFAH1B1, RAB6A, RAB6IP1, RABGAP1, SH3BP5, SPAG9, SPTBN1, TAOK1, TEGT, TRAF3IP1 39 APTX, BACE1, Beta Secretase, BTBD14B, 21 33 Cancer, Dermatological CALCOCO1, CCND1, CDK2-Cyclin D1, CHMP4A, Diseases and Conditions, COL4A3BP, CUL3, GGA2, GPBP1L1, GTPBP4, Gene Expression H2AFY, MAT2B, PAPOLA, PLEKHF2, PRR13, PTPN9, RB1CC1, RTN3, RTN4, SP2, SPEF1, SPG21, SPOP, STXBP1, SYT17, TSPYL2, UBE2Z, VAPA, XRCC4, ZEB2, ZFHX3, ZNF639 40 Ap1, AP3B1, AP3D1, AP3M1, AP3S1, ARF1, BET1, 21 33 Cellular Assembly and BLVRA, CD58, EEA1, GOSR1, GOSR2, MARCH2, Organization, Molecular PACS1, SCARB2, SCFD1, SEC22A, SEC22B, Transport, Protein SEC23A, SEC23IP, SEC24A, SEC24B, SEC24C, Trafficking SNAP23, Snare, STX6, STX7, STX16, USO1, VAMP3, VAMP4, VAMP7, VPS11, VPS41, VPS45 41 ASPN, COL11A2, COL1A2, COL3A1, DLGAP4, 21 33 Cellular Movement, EPB41L1, FBLIM1, FERMT2, FN1, FYB, HTRA1, Reproductive System IGBP1, Integrin&alpha, Integrin&beta, ITGAE, Development and Function, ITGAV, ITGB5, ITGB6, LTBP1, LTBP2, LTBP3, Cell-To-Cell Signaling and MGP, MICAL2, MID1, MYO10, PPP6C, SAPS2, Interaction SEC23B, SEC24D, SPARC, ST6GAL1, TGFB2, TGFB3, TGFBI, TSPAN13 42 BAT3, CD99 (includes EG: 4267), COL8A1, CPSF6, 21 33 Cardiovascular Disease, E3 HECT, EFEMP2, EPDR1, ERP27, FAM127B, Cardiovascular System FMNL3, GIGYF2, HRASLS3, HUWE1, KLHDC5, Development and Function, KLHL12, NFKBIA, NOTCH2NL, PCDH17, Hematological Disease PRPF40A, RAD23A, RIC8A, RPN1, RSRC2, SNRPN, STCH, STIM2, TNRC6B, UBA6, Ube3, UBE3A, UBE3B, UBQLN1, UBQLN2, UBQLN4, ZCCHC8 43 ADAD1, ADAR, ADARB1, ADD3, Adenosine 21 33 Genetic Disorder, deaminase, AKR7A2, BRAP, CCDC92 (includes Hematological Disease, EG: 80212), CCNL1, CELSR2, DCBLD2, FHL1, Protein Trafficking FXC1, GSS, KLF6, LMAN1, MCFD2, MT1E, NFAT5, OMD, PDGF BB, PHF10, PLEKHA1, RPL13, SLC6A6, SLC7A1, TIMM10, TIMM13, TIMM23, TIMM44, TIMM17A (includes EG: 10440), TIMM8A, TIMM8B, TRIOBP, ZFP36L1 44 DDIT4, DDX5, DNASE1L3, ELMOD2, Esr1-Esr1- 21 33 Molecular Transport, estrogen-estrogen, FAM130A1, GLCCI1, H1FOO, Cancer, Cell Death HNRPD, HSD17B12, ILF3, JMJD6, KHSRP, MED14, MTL5, NR3C1, PHLDA2, POGK, POLDIP2, POLR2B, PRKDC, PTMS, RNF10, RNF14, RPL34, SELENBP1, TADA2L, TIA1, TIAL1, TNFAIP1, TPST2, TRAP/Media, UNC45A, WDR37, WDR40B 45 ACBD3, APPL2, AUP1, C1ORF124, C4ORF16, 21 33 Cellular Function and CDC40, CHMP1A, DAZAP2 (includes EG: 9802), Maintenance, Molecular DCUN1D1, EPPK1, EPS15, GAD, GGA1, GRB2, Transport, Protein HGS, HRBL, LAPTM5, MIST, NISCH, PHKA2, Trafficking RAB22A, RNF11, SHIP, SIGLEC7, SKAP2, SMURF2, STAM, STAM2, STAMBP, UNC5C, USP8, USP6NL, VPS24, VPS37C, ZFYVE9 46 Adenosine-tetraphosphatase, AGRN, ANGPTL2, 21 33 Molecular Transport, Cell- ARL8B, ATP5C1, ATP5D, ATP5F1, ATP5I, ATP5J, To-Cell Signaling and ATP5J2, ATP5L, ATP5O, ATP6V0A2, ATP6V0C, Interaction, Cellular ATP6V0D1, ATP6V0D2, ATP6V0E1, ATP6V1A, Assembly and Organization ATP6V1C1, ATP6V1E1, CARS, CHMP2B, CPD, CREB1, ETV6, H+-transporting two-sector ATPase, ITIH2, ITIH4, LASS2, OPA3, PNO1, SLC18A2, SLC2A3, TCIRG1, ZNF337 47 CAPRIN1, CCDC50, DHX9, EIF3A, EIF3B, EIF3C, 21 33 Protein Synthesis, EIF3E, EIF3H, Fcer1a-Fcer1g-Ms4a2, Fcgr2, Carbohydrate Metabolism, FCGR2C, FYN, G3BP1, HNRNPU, HSPH1, IARS2, Small Molecule LUM, MDH1, NANS, PARN, RILPL2, RNMT, RPL6, Biochemistry RPL7, RPL10, RPL14, RPS2, RPS6, RPS3A, RPS4X, SFRS10, SON, STAU1, TUBB3, YTHDC1 48 ADH4, ADH5, ADH6, alcohol dehydrogenase, 21 33 Molecular Transport, ALDH2, AQP1, DHRS2 (includes EG: 10202), EIF5A, Protein Trafficking, Small FBXO33, IPO7, IPO9, MT1G, NR2F1, NR2F2, Molecule Biochemistry NUP98, NUP153, NUTF2, PAIP2, PURA, PURB, RAN, RANBP5, RPL5, RPL19, RPS7, SP3, SP4, TEAD1, TNPO1, Vegf, XPO7, YBX1, ZBTB7A, ZFPM2, ZNF197 49 AZI2, CCDC47, CMTM6, COMT, COX11, COX4I1, 21 33 Gene Expression, COX4I2, COX5A, COX6A1, COX7A2, COX7B, Behavior, Cell Signaling COX7C, COX8A, Cytochrome c oxidase, DARS, GABPB2, IARS (includes EG: 3376), Isoleucine-tRNA ligase, JTV1, JUNB, KIAA0090, LARS, LITAF, LMO3, MARS (includes EG: 4141), MFN1, MTX1, NAP1L1, OXTR, RAB3GAP2, SCYE1, SGPL1, TANK, TMBIM4, TMCO1 50 AHCYL1, BCL6, BCL2L11, BCOR, BNC1, 21 33 Cell Cycle, Cellular C1ORF19, CD79B, Cpsf, CPSF2, CPSF3, EMCN, Development, FLJ12529, FOXP3, GALNAC4S-6ST, GP1BA, GSR, Hematological System IGHM, IGJ, IGL@, Igm, ITPR2, LAGE3, LYN, Development and Function NUDT21, OSGEP, PCF11, PIGR, PRDM1, SAMSN1, SDC1, SUMO3, SYMPK, TNFSF13, TNFSF13B, TSN 51 ARF6, ARMET, C15ORF29, C6ORF211, CA12, 21 33 Cell-To-Cell Signaling and CA13, Carbonic anhydrase, CNDP2, DAP3, DDOST Interaction, Embryonic (includes EG: 1650), DMXL1, Dolichyl- Development, Gene diphosphooligosaccharide-protein glycotransferase, Expression EPAS1, GBE1, GLS, GSPT1, HIF1A, MAGT1, MAOA, NDRG1, PCBP3, PDK1, PKIB, PLOD2, PTPRG, RAB20, RGS10, RORC, RPN2, RPS9, RPS26, SAP18, SHMT2, STT3B, TPP2 52 ARMC7, BPTF, CDK4, CDK4/6, Ctbp, CTBP1, 21 33 Cell Cycle, Cellular DDX3X, DNAJA2, HCFC1, HDAC2, HNRNPA1, Assembly and HNRPH1, IFIT3, IKZF1, IKZF2, KCTD13, KPNA2, Organization, DNA LPCAT1, MBD2, MLL, NBR1, OGT, PML, PTMA, Replication, RBBP4, RBBP7, RNF12, SAP30, SIN3A, SIN3B, Recombination, and Repair SMARCA1, UTP18, VTA1, ZC3HAV1, ZMYND19 53 14-3-3(&beta, &gamma, &theta, &eta, &zeta;), 21 33 Cancer, Genetic Disorder, ARHGAP21, C16ORF80, C22ORF9, CAMSAP1L1, Cell Cycle CCNY, DOCK11, EHD1, EPB41L2, FRY, FRYL, GAPVD1, HECTD1, ITGB3, KIAA1598, LAPTM4A, LNX2, NUMB, OSBPL3, PHLDB2, PPFIBP1, PRPF38B, RACGAP1, RASAL2, RASSF8, SAPS3, SDHA, SDHAL1, SDHC, SMCR7L, Succinate dehydrogenase, SYNPO2, TBC1D1, YWHAB, YWHAG 54 C14ORF129, CD44, CDC42, CDK5RAP2, DDX58, 21 33 Immune Response, DEF6, DKC1, DMP1, EEF1G, EPB41L3, FCGR2A, Immune and Lymphatic GDI1, GDI2, HNRPH3, IQGAP, IQGAP3, IQGAP1 System Development and (includes EG: 8826), LOC196549, NOLA1, OAT, Function, Cellular PECAM1, PKN2, PRMT2, Pseudouridylate synthase, Assembly and Organization PTPRA, PTPRE, PTPRM, PTPRS, RAB4A, RAB5A, RABEP2, RPUSD4, SMYD2, STARD9, TRIM25 55 AFG3L2, ATP11B, ATPase, CCNH, CCNT2, CHD1, 21 33 Gene Expression, Cell-To- CRKRS, DHX15, EBAG9, FOXC1, GTF2H1, Cell Signaling and GTF2H2, HINT1 (includes EG: 3094), KIAA1128, Interaction, Cellular OFD1, PBX3, PRUNE, PSMC2, PSMC4, PSMD1, Development PSMD5, PSMD7, PSMD9, PSMD10, PSMD12, PSMD13, RNA polymerase II, RNF40, RSF1, SAFB, SETD2, SMYD3, SUB1, TCEA1, ZNF451 56 ABCC5, Actin, ARPP-19, BDH1, CAP1, ENC1, 21 33 Cellular Assembly and FXYD3, GAS1, GLRX5, GPC5, GSN, HNRNPC, IPP, Organization, Hair and JUN/JUNB/JUND, KIAA1274, KRAS, LIMA1, Skin Development and MAPRE2, MLPH, MMD, MSLN, MTRR, PFDN4, Function, Cellular Function PHACTR1, RAB27A, RASGRF2, RBM41, SEC11C, and Maintenance SERBP1, SYTL4, TES, TPM2, TPM3, TPM4, VBP1 57 ABCC6, Alpha Actinin, ANXA1, ANXA2, 21 33 Cellular Assembly and ARHGAP17, BLID, Calpain, CAPN2, CAPN3, Organization, Cell CAPN7, CAPN8, CASP9, CREBZF, DDX46, EZR, Signaling, Skeletal and KIAA0746, LAP3, MGC29506, MRPL42 (includes Muscular System EG: 28977), PCYT1A, PTPN1, RBM5, RPL17, RPL23, Development and Function RPL31, RPL27A, RPS21, RPS24 (includes EG: 6229), SCYL3, SORBS2, STOM, TLN1, TTN, VAT1, VCL 58 14-3-3, APC, APCDD1, BUB3, CRIPT, CRKL, 21 33 Cancer, Cell Cycle, DLG3, DR1, EHF, EMP1, GAB2, GAB1 (includes Respiratory Disease EG: 2549), HCFC1R1, HDLBP, KAL1 (includes EG: 3730), LPHN1, MACF1, MDM4, MDM2 (includes EG: 4193), MED28, MET, NF2, RAB40C, RAPGEF1, SLC7A11, SPSB3, STAT5a/b, SULF1, TFEB, TFEC, TMEM22, TP63, WT1, ZEB1, ZNF224 59 ARID5B, ASCC3, CHD2, CXCL10, DHX30, 21 33 Gene Expression, RNA DYNLL1, FAM120C, FAM98A, GPRIN3, HDAC3, Trafficking, Developmental HMGB1 (includes EG: 3146), HNRPA3, MECP2, Disorder MTA1, MTR, NCOR2, NCoR/SMRT corepressor, NUFIP2, PPP4R1, RARB, RBAK, RREB1, RSBN1, SENP6, SERPINB1, SMC6, SUMO1, TBL1Y, VitaminD3-VDR-RXR, WDR33, ZFP106, ZNF226, ZNF362, ZNF711, ZNF354A 60 adenylate kinase, AK2, AK3, AK3L1, ALKBH6, 21 33 Cardiovascular Disease, AQP3, ARL17P1, ATG2B, BMP2K (includes Cell Morphology, Genetic EG: 55589), BSDC1, C14ORF105, C16ORF61, Disorder C1ORF50, C2-C4b, CD55, CD302, COQ10B, CTDSPL2, FAM107B, GLA, GPATCH2, GPR39, HNF1A, HNMT, HSPC111, LSG1, MCCC1, MRPS18B, NRD1, PCNP, PHF2, TSC22D2, UROD, WDSOF1, ZBTB20 61 ADAMTSL4, AGK, BCL2L14, C2ORF28, CCDC6, 19 32 Connective Tissue CREB5, DNPEP, ERK, GPER, KLB, LOXL1, LPP, Disorders, Hematological MAFB, MAFF, MAFK, MPZL1, musculoaponeurotic Disease, Organismal Injury fibrosarcoma oncogene, NFE2, NFE2L1, NFE2L3, and Abnormalities NRF1, NTN4, PALLD, PPP2R2D, PRRX1, RP6- 213H19.1, SH3GLB2, SPRED1, SPRED2, SPRY, SPRY1, SPSB1, TESK1, UBA5, WWP1 62 BMI1, Cbp/p300, CBX4, COL5A2, ERBB2IP, 19 32 Skeletal and Muscular HIST2H2AA3, HOXA2, HOXA3 (includes EG: 3200), System Development and HOXB8, HOXB6 (includes EG: 3216), HOXD4, Function, Embryonic MAPKAPK3, MEIS1, PBX1, PBX2, PCGF2, PDZD2, Development, Tissue PHC1, PHC2, PHC3, PKNOX1, PKP4, POU3F2, Development RABGEF1, RAPGEF2, Ras, RNF2, RPS6KC1, RYBP, SHOC2, Sox, SOX4, SOX11, SOX12, UBP1 63 ARCN1, CNKSR2, COPA, COPB1, COPB2, COPG, 19 32 Cellular Assembly and COPG2, COPZ1, COPZ2, DOCK8, ENSA, Erm, Organization, Cellular GALNT1, GYS2, Insulin, KIAA1881, LRRC7, MCF2, Compromise, Nervous MOBKL2B, MSN, NME7, NT5C2, PELO, peptide- System Development and Tap1-Tap2, PTGFRN, RDX, RHOC, RHOJ, RHOQ, Function SLC25A11, SLC25A12, SPN, TAPBP, TCHP, TMED9 64 ACO1, AMPD3, AOAH, C1q, C1QBP, C1QC, C1S, 19 32 Cellular Function and C3-Cfb, CD93, CFH, CFI, Complement component 1, Maintenance, Small CP, CR1, EGR1, FAM83C, FTH1, FTL, IGKC, Molecule Biochemistry, IREB2, KIAA0754, KRT10, KRT6A, LECT1, MLX, Gene Expression MLXIP, MNT, MXD1, NAB1, NOPE, PAPPA, PNMA2, SAFB2, TNS3, ZNF292 65 ALAD, ARL5A, CBX1, CBX3, CBX5, DIAPH2, 19 32 Gene Expression, DNA EEF1B2, eIF, EIF1, EIF5, EIF1AX, EIF2S3, EIF5B, Replication, EZH1, HELLS, IFITM2, INSR, IPO13, JAK1/2, LBR, Recombination, and METAP2 (includes EG: 10988), MKI67, MKI67IP, Repair, Protein Synthesis OGN, OLFM2, RNF13, RWDD4A, SEZ6L2, SFRS5, SP100, SURF6, Tk, TK2, ULK2, ZFAND5 66 3-hydroxyacyl-CoA dehydrogenase, ACAT1, Acetyl- 19 32 Amino Acid Metabolism, CoA C-acetyltransferase, CALU, CSE1L, CSTB, Small Molecule DNAJB1, F8A1, FAM120A, FDFT1, GCLC, GCLM, Biochemistry, Drug HADHA, HADHB, HSD17B4, HSP90AB1, HSPA5, Metabolism IBSP, IFITM1, LDL, LMO7, NPC2, OSBP, PDCD6, PON1, PON2, RCN1, S100A9, SCARF1, SCARF2, SFXN3, SPTLC1, TMEM43, TRIP12, XPO1 67 BAG5, CCT2, CCT4, CCT5, CCT8, CCT6A, 19 32 Post-Translational CDC37L1, Chuk-Ikbkb-Ikbkg, CORO1C, FKBP4, Modification, Protein FKBP51-TEBP-GR-HSP90-HSP70, HIST1H2BK, Folding, Drug Metabolism HIST4H4 (includes EG: 121504), HSBP1, Hsp70, HSPA4, HSPA6, HSPA9, JMJD2A, MAN2B2, MAP3K1, MAP3K3, PACRG, PDCL, PHF20L1, PTGES3, RPAP3, RPL3, RPL10A (includes EG: 4736), RPL37A, RPS11, RPS23, RPS27L, TCP1, WDR68 68 BGN, CTGF, DCN, ELA2, ELN, ERBB3, ERBB4 19 32 Cellular Movement, ligand, FBN1, FCN2, Fibrin, GLB1, Igfbp, IGFBP4, Cancer, Cell-To-Cell IGFBP5, IGFBP7, LGMN, MMP2, MMP10, MMP17, Signaling and Interaction MMP25, NPEPPS, PAPPA2, PCSK6, RECK, S100A4, SERPINA1, SERPINA3, SPOCK1, THBS1, THBS2, TIMP2, TMEFF2, TNFAIP6, VCAN, ZBTB33 69 AKT2, Alcohol group acceptor phosphotransferase, 19 32 Amino Acid Metabolism, BRAF, CCR7, CDC42BPA, CDK6, CFL1, CK1, Post-Translational Cofilin, CSNK1A1, CSNK1G1, CSNK1G3, DAPK1, Modification, Small DDX24, DPYD, DYRK1A, EIF2AK2, EIF4E, Molecule Biochemistry FAM98B, FOXO1, GRK6, HIPK1, LIMK2, MIB1, PA2G4, PDS5B, PRKRIP1, PRKX, RAE1, SGK1, SNX5, SSH2, TMEM9B, TPI1, TXNL4B 70 APLN, COL11A1, Cyclin B, CYP2R1, DDX42, 19 32 Nutritional Disease, Cell DNM3, DNM1L, Dynamin, EWSR1, HIPK2, HMGA1, Cycle, Hair and Skin HSPB8, MPHOSPH8, NFYA, NRGN, p70 S6k, Development and Function PACSIN2, PDPK1, PFN1, RANBP9, RPS6KA3 (includes EG: 6197), RPS6KB1, SLC9A1, SUPT7L, TAF1, TAF2, TAF4, TAF8, TAF10, TAF11, TAF13, THRA, TP53INP1, VDR, XPO6 71 ATXN2L, C5ORF22, CASP2, Caspase, CENPO, 19 32 Cellular Function and DGCR8, DHPS, E2f, EIF2AK3, EIF4G2 (includes Maintenance, Connective EG: 1982), FAM33A, GAS2L1, GOLT1B, Hdac, Tissue Development and HDAC4, HDAC9, HIST1H2AB, HOPX, LRDD, Function, Viral Function MOCS2, MTCH1, NUSAP1, PSME3, RNASEN, ROCK1, RPL9 (includes EG: 6133), SNRPC, SPEN, STK24, SUMO2 (includes EG: 6613), TMEM126B, WBP4, XAF1, ZBTB1, ZNF160 72 ARRB1, CD164, CPNE8, DDX27, DNAH3, DNAL1, 19 32 Genetic Disorder, GLRX2, NADH dehydrogenase, NADH2 Neurological Disease, Cell- dehydrogenase, NADH2 dehydrogenase (ubiquinone), To-Cell Signaling and NDUFA3, NDUFA4, NDUFA5, NDUFA6, Interaction NDUFA11, NDUFA12, NDUFAB1, NDUFB1, NDUFB2, NDUFB4, NDUFB5, NDUFB7, NDUFB8, NDUFB10, NDUFC2, NDUFS1, NDUFS5, NDUFS8, NDUFV1, NDUFV3 (includes EG: 4731), RPL7L1, RPS17 (includes EG: 6218), RTF1, SCYL2, ZRANB2 73 ANK1, ARID4B, ATF7IP2, B4GALT1, B4GALT5, 19 33 Gene Expression, RNA B4GALT6, B4GALT7, CCNL2, CDC2L2, CTSD, Post-Transcriptional CYP7B1, Esr1-Estrogen-Sp1, FUSIP1, Modification, Carbohydrate Galactosyltransferase beta 1,4, IFITM3, MGEA5, MI- Metabolism ER1, MRC1, MUC5B, NFYC, PDHX, POGZ, RHAG, SFRS7, SFTPA2B, SLC11A1, SP1, SUDS3, TFAM, TFB2M, TRA2A, TXNDC12, WDFY3, ZFYVE20, ZNF587 74 ADAMTS5, AIF1, COP1, CYSLTR1, ERAP1, 18 31 Cell Signaling, FAM105B, GALNS, GNL1, IL-1R, IL-1R/TLR, IL1B, Carbohydrate Metabolism, IL1R1, 1L1R2, IRAK, IRAK1, IRAK2, IRAK3, Nucleic Acid Metabolism IRAK4, IRAK1/4, MYLK3, NUCB2, PELI1, PLXDC2, PTP4A1, SCUBE2, SEPP1, TICAM2, TIFA, TIRAP, TLR4, TLR8, TLR10, UAP1, UGDH, ZC3H12A 75 ABCC9, AIM1, AJAP1, APOD, ARL4A, BCL9, Bcl9- 18 31 Gene Expression, Cbp/p300-Ctnnb1-Lef/Tcf, CDH9, CELSR1, CST4, Embryonic Development, CTNNB1, DKK3, GPR137B, Groucho, HES1, ID4, Tissue Development KCNIP4, L-lactate dehydrogenase, LDHA, LDHB, LEF1, LEF/TCF, LEO1, MGAT5, MUC6, NLK, PLS3, PTCH1, SFRP2, TAX1BP3, TCF4, TCF7L2, TLE1, TLE4, UEVLD 76 AHR, Ahr-aryl hydrocarbon-Arnt, C12ORF31, 18 31 Gene Expression, Hepatic CAMK2N1, COL1A1, COL5A1, COL5A3, CSF1, System Disease, Esr1-Estrogen, GPR68, GUCY1B3, HOOK3, IFNGR1, Dermatological Diseases KLF3, LOX, MRC2, MSR1, NFIA, NFIB, NFIC, and Conditions NFIX, Nuclear factor 1, P4HA1, RAB5B, RIN2, RIN3, RRAS2, SERPINH1, SKI, SLC25A5, SLC30A9, SLC35A2, SWI-SNF, TPD52L3, TRAM2 77 C10ORF119, DNA-directed DNA polymerase, 18 31 DNA Replication, EIF2AK4, H2AFX, HUS1, IL1, LNPEP, MCM4, Recombination, and MCM10, Mre11, MRE11A, ORC2L, ORC3L, ORC6L, Repair, Cell Cycle, Gene PAPD5, POLE4, POLH, POLS, RAD1, RAD17, Expression REV1, RFC3, RPA, RPA3, TAX1BP1, TERF1, TERF2IP, TNKS, TNKS2, TNKS1BP1, TP53BP1, XAB2, XPA, XRCC5, ZBTB43 78 ACTR1B (includes EG: 10120), ACVR1, ACVR1B, 18 31 Embryonic Development, ACVR2A, ACVRL1, CUL5, FBXO3, FBXO24, Tissue Development, FBXO34, FGD6, FOXH1, INHBC, NARG1, NAT5, Organismal Development PLEK, PREB, SAMD8, SAR1A, SMAD2, Smad2/3, SMURF1, SNX1, SNX2, SNX4, SNX6, TBL2, Tgf beta, TGFBR, TGFBR1, TRIM35, Type I Receptor, UHMK1, VPS29, VPS35, ZFYVE16 79 Ahr-Aip-Hsp90-Ptges3-Src, ANKRD12, ATRX, 18 31 Cell Morphology, CUGBP1, CXCL12, DNAJ, DNAJC, DNAJC1, Hematological System DNAJC3, DNAJC7, DNAJC8, DNAJC10, DNAJC11, Development and Function, DNAJC14, DNAJC15, EIF2AK1, FEZ2, FUBP1, Immune and Lymphatic Hsp90, ICK, IFNAR2, IRF6, LATS2, LOXL2, System Development and MARCKS, MBNL1, NEK1, NLRP3, PPP5C, PRKRIR, Function SAV1, STK3, STK4, TXNL1, ZNF350 80 ANKH, BTBD1, CBFB, CHST8, CK1/2, CLPP, 18 31 Post-Translational COL4A1, CSF1R, CSNK2A1, CSNK2B, CTNNBL1, Modification, Cancer, Gene CXCL6, GALNT2, GALNT3, GALNT4, GALNT7, Expression Glutathione peroxidase, GPX3, KYNU, LOC493869, LSAMP, LTA4H, MUC1, PCSK7, peptidase, Polypeptide N-acetylgalactosaminyltransferase, POU2F2, PRNP, PRNPIP, RUNX1, SEPHS1, SPP1, TDP1, TOP1, TRIM5 81 AAK1, Adaptor protein 2, AMD1, AP2A1, Arf, ARF3, 17 31 Cellular Function and ARFGEF1, ARFIP1, DLEU2, EHBP1, EHD2, Fcgr3, Maintenance, Cell FCGR3A, GABAR-A, GABRB1, GABRP, GBF1, Signaling, Molecular HIP1, LAMP1, LDLRAP1, PABPN1, PICALM, RIT1, Transport RPL4, RPL11, RPL15, RPL21, RPL24, RPL27, RPL28, RPL12 (includes EG: 6136), RPLP2, SHC1, SYT6, SYT7 82 ANAPC1, ANTXR, ANTXR1, ANTXR2, BIRC2, 17 31 Post-Translational BIRC6, CBL, E3 RING, PARK2, Proteasome, RNF5, Modification, Protein SORBS1, SUGT1, TCEB1, UBE2, UBE2A, UBE2B, Degradation, Protein UBE2D1, UBE2D2, UBE2D3, UBE2E1, UBE2E2, Synthesis UBE2F, UBE2G1, UBE2G2, UBE2H, UBE2I, UBE2J1, UBE2N, UBE2R2, UBE2V1, UBOX5, UBR3, XIAP, ZMYM2 83 3alpha-hydroxysteroid dehydrogenase (A-specific), 16 30 Drug Metabolism, Genetic AKR1C1, AKR1C2, AKR1C3, CLSTN2, COL6A1, Disorder, Lipid Metabolism COL6A3, DPYSL2, DPYSL3, DPYSL4, DPYSL5, DYRK2, FAT, GOLGA3, GRIP1, IGF1, KIF5B, KLC1, KLC2, KLF9, ME1, MGA (includes EG: 23269), NCOA4, OSBPL7, SERCA, SNED1, SRA1, SRD5A1, SRR, STRBP, T3-TR-RXR, Thyroid hormone receptor, TRAK1, TRAK2, Trans-1,2- dihydrobenzene-1,2-diol dehydrogenase 84 ARF4, ARL6IP1, BRCC3, BTRC, CAND1, Cyclin D, 16 30 Cellular Development, Cyclin E, E3 co-factor, FBXW7, FBXW11, FZR1, Hematological System ITCH, JUN, MEF2B (includes EG: 4207), MIA3, Development and Function, N4BP1, NEDD9, PXDN, Scf, SEC63, SEC61A2, Immune Response SEC61B, SEC61G, SERP1, SKP1, SKP2, SLC30A1, SNAPC5, SUMF2, TAL1, TCF12, TCF20, Tcf 1/3/4, TLOC1, WEE1 85 ALOX5AP, B2M, CD4, CD74, CD1C, CD1D, CIITA, 16 30 Immune Response, Cell- CLEC7A, CSF2, Csf2ra-Csf2rb, CTSS, FCER1G, To-Cell Signaling and HLA-DMA, HLA-DOA, HLA-DPA1, HLA-DPB1, Interaction, Immunological HLA-DQA1, HLA-DQB1, HLA-DQB2, HLA-DRA, Disease HLA-DRB1, HLA-DRB4, Ifn alpha, IL12B, IL5RA, LAMP2, LYPLA3, MHC Class II, MHC II-&beta, Mhc2 Alpha, MOG, RBM3, RFX5, SLC39A6, SSBP1 86 ARHGAP1, ARHGAP5, ARHGAP6, ARHGAP8, 16 30 Cell Signaling, Cell-To- ARHGAP9, ARHGAP15, ARHGAP29, ARHGEF2, Cell Signaling and BNIP2, CADM1, CASK, CD226, CNKSR3, DLC1, Interaction, Tissue EVI5, F11R, GRLF1, INPP5A, Itgam-Itgb2, JAM, Development JAM2, JAM3, KIFAP3, LIN7B, MAGI, MAGI1, MLLT4, NRXN2, PVR, RAC1, Ras homolog, RhoGap, SH3BP1, SYNPO, THY1 87 BRD4, CAM, CLDN1, CLDN10, CLDN11, CLDN15, 16 30 Cellular Movement, DDX18, Guk, INADL (includes EG: 10207), ITGB1, Nervous System LAMA2, LAMB1, LAMB3, LAMC1, MPDZ, MPP2, Development and Function, MPP5, Nectin, NPNT, OCLN, PLEKHA2, Pmca, Gene Expression PPP1R9A, PPP1R9B, RAB21, TBX5, TEAD2, TEAD3, TEAD4, TGOLN2 (includes EG: 10618), TSPAN, TSPAN3, TSPAN6, WWTR1, YAP1 88 ASH1L, AURKA, CCDC71, CENTD2, CHFR, 16 30 Cell Morphology, Cellular CLDND1, ERCC6, GSTA4, GTF2A1, HIST2H3D, Assembly and Histone h3, JMJD2C, KIAA0265, LARP2 (includes Organization, Cell Cycle EG: 55132), LOC26010, MAGED2, MTUS1, NAIP, NFATC2IP, NUP37, NUP43, PARP10, SAR1B, SEC13, SEC31A, SETMAR, Taf, TFIIA, TFIIE, TFIIH, TM4SF18, UBB, WDR1, ZBTB44, ZDHHC11 89 ANGPT2, ATP2B4, C7ORF16, Calcineurin protein(s), 16 30 Skeletal and Muscular CBLB, CHP, CKM, CTSL2, DBN1, DLG1, FBXO32, System Development and GPI, GRIN2C, MAPK9, MARCKSL1, MEF2, Function, Tissue MEF2A, MEF2C, MYOZ2, Nfat, NFAT complex, Morphology, NFATC2, NIPBL, ODF2L, PARP14, PDDC1, Pp2b, Dermatological Diseases PPP3CA, PPP3CB, PPP3CC, PRSS23, RCAN1, SOD1, and Conditions TRAPPC4, XPNPEP1 90 AFF1, ALP, ANAPC13, ARNT, BMP, BMP7, 16 30 Cellular Development, BMP8A, C10ORF118, CDC16, CDC27, CEP135, Connective Tissue CFDP1, CTDSP2, CXXC5, EXPH5, KIAA0256, Development and Function, KIAA0372, KIAA1267, MAST4, PAX8, PRDM4, Rar, Cell Signaling RNF123, Smad, SMAD1, SMAD3, SMAD5, SMAD9, Smad1/5/8, TMEM57, ZDHHC4, ZMIZ1, ZNF83, ZNF251, ZNF557 91 ABCC1, ACTA2 (includes EG: 59), CAMLG, CCND2, 16 30 Tissue Morphology, CDKN2C, CDKN2D, CES2 (includes EG: 8824), Dgk, Cancer, Reproductive ERP29, ESD, ETS1, Fgf, FGF13, FKBP5, Glutathione System Disease transferase, GSTM2, GSTM1 (includes EG: 2944), GSTO1, IFI16, INHBA, ITGB2, MGST1, MGST2, MTHFD2, NFE2L2, PCYT1B, PMF1, PPIB, PXR ligand-PXR-Retinoic acid-RXR&alpha, Rb, RUNX2, TCF3, THRB, TYMS, ZNF302 92 ADAM9, ADAM10, ADAM12, ADAM17, ADAM21, 16 32 Developmental Disorder, ADAM23, ADAM30, ADK, APBA2, APLP2, APP, Neurological Disease, BLZF1, C5ORF13, CHN1 (includes EG: 1123), Organ Morphology ERBB4, GNRHR, GORASP2, ITM2B, Metalloprotease, NCSTN, NECAB3, Notch, PCDHGC3, PPME1, PROCR, PSEN1, Secretase gamma, SH3D19, SLC1A2, SPON1, SPPL2A, TM2D1, TMED2, TMED10, YME1L1 93 ANAPC10, CCNG2, EEF2, FGFR1OP, GLUL, 15 29 Cancer, Cell Death, Glycogen synthase, HSPD1 (includes EG: 3329), L-type Reproductive System Calcium Channel, M-RIP, MAP2K1/2, Mlcp, MPST, Disease MYCL1, PARK7, PP1, PP1/PP2A, PPP1CB, PPP1CC, PPP1R8, PPP1R10, PPP1R11, PPP1R12A, PPP2CA, PPP2R1B, PPP2R2A, PPP2R2B, PPP2R5C, PRDX1, PTPN7, RAB18, RAB11A, RAB11FIP1, RALA, RAP1A, WDR44 94 AKAP, AKAP1, AKAP4, AKAP7, AKAP8, AKAP9, 15 29 Protein Synthesis, AKAP13, AKAP10 (includes EG: 11216), BMPR, Molecular Transport, C3ORF15, CBFA2T3, CD40, FLJ10357, GLO1, Protein Trafficking LZTS1, MYCBP, PDZK1, Pka, Pki, PRKACB, PRKAR1A, PRKAR2A, PSMC3IP, RAB13, Rap, RFX2, RPL30, SEP15, SLC30A7, sPla2, SUSD2, TYRP1, UGCGL1, XCL1, ZNF652 95 AGTPBP1, ANK2, ANK3, BCL10, CASP7, CD3, 15 29 Cell Death, Hematological CD3E, CFLAR, Ciap, CTLA4, DIABLO, ERC1, Disease, Immunological FASLG, FOXP1, IKBKB, IKK, ITPR, MALT1, Disease MOAP1, NF-&kappa; B, PDPN, PPFIA1, PPM1B, PTPN2, PTPRC, ROD1, SOS2, TCR, TNFAIP3, TNFRSF10A (includes EG: 8797), TNFRSF10B, TNFSF10, TNIP2, TRAF3IP2, TXNRD1 96 ANKRD17, BCL3, C1ORF41, C2ORF30, CCDC82, 15 29 Inflammatory Disease, EFR3A, ERMP1, FILIP1L, FOXO3, HBP1, Hd- Renal Inflammation, Renal perinuclear inclusions, HDAC8, HERC3, Hsp27, Ikb, and Urological Disease KLF5, MON2, MRCL3, MYO6, Nos, NSFL1C, PHF17, RARA, RBL2, SFRS15, SMG5, SMG7, SNX3, SPG20, Tnf receptor, TNFRSF1A, TXLNA, Ubiquitin, UFC1, VHL 97 AK5, BMI1, BNIP3 (includes EG: 664), BTBD11, 15 29 Tissue Development, Cell C10ORF58, CAV1, CBX4, COG5, DBT, DDEF1, Cycle, Cellular FLJ38973, HIST2H2AA3, JARID1C, LOC653441, Development MCRS1, MEG3 (includes EG: 55384), NFX1, NUCKS1, PCGF2, PHC1, PHC2, PRKD1, PXN, RING1, RNF2, RPL7, RPL22, RYBP, TERT, TMEM70, VEGFA, XRCC5, YWHAQ (includes EG: 10971), ZNF313, ZRANB1 98 ADC, ANXA4, CD244, CKS2 (includes EG: 1164), 14 28 Cancer, Gastrointestinal CTSB, CTSL2, EDNRA, ELK3, FARS2, FBN1, FOS, Disease, Tumor GCNT1, GNL2, heparin, HNRNPA2B1, IL15, IL1R1, Morphology KIAA1600, MGAT4A, MSLN, NUBP1, Ornithine decarboxylase, P2RY1, PCNX, PUNC, RPS18, SERPINE2, TCEA1, TFPI, THBS4, TNC, TXNRD1, VWA1, ZNF33A 99 ANGEL2, AP2A1, ARL1, ARMC1, BRF2, C7ORF58, 14 25 RNA Post-Transcriptional C9ORF64, CCDC59, CHP, COPB2, DPM1, HNF4A, Modification, Cellular KIAA1704, MRPL3, MRPL32, MRPS21 (includes Assembly and EG: 54460), PDXDC2, SF3B1, SLC44A1, SLMO2, Organization, Carbohydrate SRPRB, SSR3, TBC1D20, TIMM23, TMEM33, Metabolism TNPO3, TOE1, TXNL4B, ZNF644 100 ABI1, ACTR2, ACTR3, AHNAK, Alpha actin, 14 28 Cellular Assembly and ARHGDIA, Arp2/3, ARPC2, ARPC3, ARPC4, Organization, Cell ARPC5, ARPC1B, ARPC5L (includes EG: 81873), Signaling, Cell C3ORF10, C8ORF4, CACNB2, CORO1B, CTTN, F Morphology Actin, FER1L3, G-Actin, HCLS1, IQGAP2, IQUB, MAST2, NCF4, NCKAP1, PCDH24, PFN, Pkc(s), SSH1, Talin, WASF1, WASF2, WIPF1

EXAMPLE 3 Formulation of Treatment for Epithelioid Hemangioendothelioma

Biopsied tumor tissue from the patient was assayed for gene expression using Agilent transcription mRNA profiling and compared to the normal expression profile obtained from a database. 7,826 Genes had expression ratio thresholds of 3-fold up- or down-regulation, and a significance P-value of 0.05.

Using a tool provided by Ingenuity Systems, the of 7,826 genes were subjected to an algorithm which finds highly interconnected networks of interacting genes (and their corresponding proteins). Protein/protein interaction is determined directly from the research literature and is incorporated into the algorithm. These findings were then further analyzed to find particularly relevant pathways which could provide potential therapeutic targets or, if possible, clusters of interacting proteins which potentially could be targeted in combination for therapeutic benefit.

An initial analysis was done to confirm that the global gene expression from the tumor sample was generally consistent with a priori expectations for a neoplasm of this type. The networks that were assembled by the protein interaction algorithm for up- or down-regulated genes were analyzed with respect to the cellular and organismal functions of their individual component genes. The four top-scoring networks (with respect to the interconnectedness of their component genes) were greatly enriched in the following functions:

Cellular Growth and Proliferation Hematological System Development and Function Immune Response Endocrine System Disorders Immunological Disease Metabolic Disease Viral Function Carbohydrate Metabolism Molecular Transport Connective Tissue Disorders Organismal Injury and Abnormalities

This overall pattern is consistent with what one might expect from the global gene expression of an endothelial cell-derived tumor as compared to normal blood vessel, which helps to confirm that the signals are from the tumor sample and that the integrity of the gene expression milieu has been maintained. The entire list of networks and their associated functions is in the table at the end of this example.

The list of over- or under-expressed genes was scored for associated negative or adverse cellular functions. The highest scoring category was liver proliferation. (FIG. 9) Several cardiovascular gene functions were also high, probably as a result of normal blood vessel tissue being used as the control, i.e., an apparent down-regulation of many genes normally expressed in blood vessels. Both of these classes of findings are consistent with global gene expression patterns of tumor tissue compared with normal blood vessel tissue.

Most notably, a large, highly interacting network of regulated genes was found in the tumor sample centered on the angiotensin II receptor, type 1 (AGTR1; AT1R) including pre-angiotensinogen, the gene product precursor for angiotensin II, as shown in FIG. 10.

The angiotensin pathway (or renin angiotensin system (RAS)) has a well established role in mediating blood pressure and volume, both systemically, and more recently in local organ systems. Indeed, the pathway is targeted therapeutically in the treatment of hypertension both via reduction of angiotensin II (ACE inhibitors) and by blocking AT1R receptors. More recently, however, ATIR has been implicated as a potential therapeutic target in a number of cancers, both through antimitotic and anti-vascularization mechanisms (Ino, K., et al., British J. Cancer (2006) 94:552-560.e; Kosugi, M., et al., Clin. Cancer Res. (2006) 2888-2893; Suganuma, T., et al., Clin. Cancer Res. (2005) 11:2686-2694), however, clinical and epidemiological results have been mixed (Deshayes, F., et al., Trends Endocrinol Metab. (2005) 16:293-299). It does represent an attractive target however, since there are numerous available ATR1 blockers such as the sartans, which have been widely prescribed (chronically) for hypertension.

Additionally, it is known from the literature that AT1R trans-activates the EGF receptor (EGFR) (Ushio-Fukai, M., et al., Arterioscler Thromb Vasc Biol (2001) 21:489-495) which is a demonstrated player in oncogenic processes and an established target for several cancer drugs (e.g., Erbitux™, Iressa®, Tarceva®). In the tumor sample, both EGFR and its family member and interacting receptor EGFR2 (Her2/Neu; erbb2) which is the target for the anti-cancer drug Herceptin®, are also significantly up-regulated. It should be noted, that another analyst found that EGFR protein, as demonstrated by immunohistochemistry (IHC) is not seen in the tumor sample. However, there is a body of literature demonstrating that the presence of the EGFR protein target, as demonstrated by IHC, is actually not a good predictor of clinical response to anti-cancer drugs that target EGFR (Chung, K. Y., et al., J. Clin. Oncol. (2005) 23:1803-1810.i). EGFR copy number change (which would be reflected in increased mRNA as detected by expression profiling) is a better predictor (Ciardiello, F., et al., N. Engl. J. Med. (2008) 358:1160-1174). A further validation study was performed using copy number which found that EGFR was indeed mutated.

One caveat of this analysis is the possibility of “contamination” of tumor tissue with normal liver tissue, which could potentially be a confounding variable. In fact, both AT1R and EGFR are expressed in higher amounts in normal liver than in normal blood vessel, which at least in principal could explain the over-expression of these two targets. One finding, however, makes this possibility less likely: While the exact mechanism for the trans-activation of EGFR by ATR1 activation is unknown, there is evidence that a key intermediate is the gene NOX1 (Ding, G., et al., Am. J. Physiol. Renal Physiol. (2007) 293:1889-1897). NOX1 was highly up-regulated in the tumor sample (FIG. 11), yet is not at all highly expressed in normal liver relative to blood vessel, indicating a tumor source for the very high NOX1 signal. NOX1 itself does not represent a therapeutic target, however it may be an important marker for activation of the ATR1-EGFR interaction. Thus, this both confirms the AT1R/EGFR pathway activation, and is consistent with tumor localization of the up-regulated genes.

There is a second issue which makes the angiotensin pathway a potentially attractive target to emerge from this analysis. While the internal validation described above indicates that the tissue samples used for the gene expression study are indeed primarily tumor tissue, any profiling based on macro-dissection of tissue always has the possibility of measuring some signal from tumor stroma as opposed tumor cells per se. The angiotensin pathway, however, has been implicated in tumor biology both via a mitotic effect and a tumor vascularization effect. Angiotensin receptors localized to the stroma are thought to play a role in tumor vascularization, while tumor receptors are thought to mediate a mitotic effect. Therefore there might be a relevant to therapeutic role of decreasing activity in this pathway regardless of whether the overexpression is happening in tumor or the stroma.

The overall gene expression findings are consistent with an endothelial-derived tumor in the liver, based on global gene regulation. In addition, one particular pathway emerged from the analysis which has several potential points of therapeutic intervention that would not have been considered as part of the standard oncology approaches. In particular, the precursor angiotensin II (AGT), its receptor (AT1R; AGTR1) are both up-regulated, as well as EGFR and EGFR2 (Her2/Neu; erbb2). AGTR1 transactivates EGFR, which in turn heterodimerizes with EGFR2; the activated receptor is known to play a role in oncogenesis. Therefore, targeting AT1R, EGFR or EGFR2, possibly in combination, is suggested. Furthermore, the angiotensin pathway may represent a particularly robust target with respect to localization, as there may be benefit to blocking both tumor or stroma activity.

For the VEGF pathway, algorithm 1 gives probability of pattern being produced by chance (Π) as 1×10⁻⁷.

The total pathway elements (q)=25, which are 1 ligand (VEGF), 1 receptor (VEGFR), 16 downstream in survival branch (PI3K, PLCV, PIP2, PIP3, DAG, IP3, CA2, 14-3-3σ, XHR, AKT, eNOS, PKC α/β, BAD, BcI XL, NO, BcI 2), 7 in proliferative branch (SHC, GRB2, SOS, Ras, c-Raf, MEK 1/2, ERK 1/2). The total aberrant genes consistent with hypothesis (n)=12; which are 1 ligand (VEGF), 1 receptor (VEGFR), 7 downstream in survival branch (PI3K, PLCV, 14-3-3σ, XHR, AKT, PKC α/β, BcI XL), 3 in proliferative branch (SHC, GRB2, ERK 1/2). The total number of possible pathways (N): Assume ˜200 based on XXX canonical pathways within Ingenuity, with a cut-off probability (p)=0.05.

EXAMPLE 4 Formulation of Treatment of Malignant Melanoma in a Patient

Tumor samples (melanoma metastases to lung) and normal tissue samples from the same patient (surrounding lung tissue) were obtained at biopsy. Affymetrix transcription profiling data (Hu 133 2.0 Plus), consisting of tumor vs. control gene expression ratios were generated using mRNA from this tissue. These data were filtered to obtain genes with an expression ratio threshold of 1.8-fold up- or down-regulation, and a significance P-value of 0.05.

In addition, DNA samples were processed using Affymetrix SNP Array 6.0 to determine genomic segments of amplification or deletion, referred to herein as Copy Number/Loss of Heterozygosity (CN/LOH) analysis. Individual genes contained in the amplified or deleted segments were determined using a genome browser.

A total of 5,165 genes from transcription profiling were passed on to network analysis. The filtered list of 5,165 genes was subjected to an algorithm (Ingenuity Systems) that finds highly interconnected networks of interacting genes (and their corresponding proteins). Proprietary software tools were used to integrate the networks with the CN/LOH data. Protein/protein interaction is determined directly from the research literature and is incorporated into the algorithm. These findings were then further analyzed to find particularly relevant pathways, which could provide potential therapeutic targets or, if possible, clusters of interacting proteins that potentially could be targeted in combination for therapeutic benefit.

In addition to the dynamic networks created on the fly from the filtered genes, expression and CN/LOH data can be superimposed onto static canonical networks curated from the literature. This is a second type of network analysis that often yields useful pathway findings.

Before looking for potential therapeutic targets, an initial analysis was done to confirm that the global gene expression from the tumor sample was generally consistent with a priori expectations for a neoplasm of this type. This serves as a crude measure of quality control for tissue handling and microarray processing methodology. The networks that were assembled by the protein interaction algorithm from the filtered list of up- or down-regulated genes were analyzed with respect to the cellular and organismal functions of their individual component genes. The three top-scoring networks (with respect to the interconnectedness of their component genes) were greatly enriched in the following functions:

Molecular Transport Cellular Development Lipid Metabolism Cancer Reproductive System Development and Gene Expression Function Cell Signaling RNA Post-Transcriptional Modification

This overall pattern is consistent with what one might expect from the global gene expression of a tumor sample, as compared to normal tissue. These are very high-level general categories and by themselves do not point towards a therapeutic class. However they help to confirm that we are looking at signals from the tumor sample and that the integrity of the gene expression milieu has been maintained. The entire list of networks and their associated functions is set forth in Table 4.

In addition to a network analysis of the filtered list of 5165 regulated genes, the entire filtered list of genes was scored for associated cellular functions. The highest scoring category was cancer. (FIG. 12) Note also the high scoring of cell proliferative gene functions: Cell Cycle, Cellular Growth and Proliferation, Gene Expression. Both of these classes of findings are consistent with global gene expression patterns of tumor tissue compared with normal tissue.

Three major findings emerged from the analysis. They are presented below in order of the judged strengths of the emergent hypotheses. It should be noted that while the first hypothesis is scientifically stronger, the key drugs that target the pathway are still in clinical trials. An already approved drug may more easily target the pathways in the second and third hypotheses.

First Hypothesis

Cyclin-dependent kinase 2 (CDK2) was found to be highly up-regulated (19-fold) in the tumor. CDK2 is necessary for cell cycle progression from G1 to S phase (FIG. 13). Thus, inhibition of CDK2 would be expected to arrest cells in G1 and therefore block cell division. However, when tested in cancer cells, CDK2 inhibition does not arrest cell growth, with one notable exception: melanoma cells. This has led to the notion that CDK2 is an attractive target in melanoma. The lack of effect in other cancer cells, and more importantly, in normal cells, implies that CDK2 inhibition may lead to melanoma-specific cell cycle arrest, with low toxicity (Chin, et al., 2006; Du, et al., 2004).

FIGS. 14 and 15, below, are more detailed views of the G1/S checkpoint pathway and the role of CDK2 in its regulation. Expression values and CN/LOH results from the tumor tissue (expressed as fold-change compared to control tissue) are overlaid in the diagram.

In this patient's tumor sample, there is an additional reason to suspect that CDK2 inhibition may be effective: CDK2 is normally deactivated by interaction with protein kinase C-eta (PKCeta; PRKCH; Kashiwagi, et al., 2000). The CN/LOH analysis of the tumor indicates that in the tumor sample PKCeta is deleted, suggesting that CDK2 may be permanently in its more active form. Thus, CDK2 is both transcriptionally up-regulated, and post-transcriptionally, is devoid of the deactivating influence of PKCeta. Thus, these data provide information that indicates the presence of CDK2 activity, a parameter not measured directly.

CDK2 activation leads to hyperproliferation via its phosphorylation of, and subsequent de-activation of retinoblastoma protein (Rb), a tumor suppressor. Active, de-phosphorylated Rb binds to the transcription factor E2F and prevents activation by E2F of genes necessary for cell cycle progression. Thus, phosphorylation of Rb by CDK2 prevents this cell cycle arrest by Rb. Although the mRNA levels of Rb and E2F are not up-regulated in the tumor sample, their activity is a function more of their phosphorylation state than transcription level. The up-regulation and chronic activation of CDK2 would produce a higher phosphorylation state and hence lower activity of Rb, and greater activity of E2F in promoting cell proliferation. As described above, in preclinical studies, melanoma cells are particularly vulnerable to CDK2 inhibition compared with normal tissues and other cancer cells (Tetsu and McCormick, 2003), which makes CDK2 a particularly attractive target. There are several CDK2 inhibitors in development, notably flavopiridol and CYC202, with trials ongoing for melanoma and other cancers.

Table 5 summarizes the evidence from the integrated expression and CN/LOH studies bearing on the hypothesis of CDK2 pathway dysregulation in the tumor. CDK2 was highly up-regulated and constitutes the strongest evidence. PRKCH is deleted, which also strongly supports the idea of CDK2 hyperactivity. We did not see up-regulation of Rb or E2F, however, we would not expect to, as these two pathway members are regulated by CDK2 at the level of protein functional activity, not transcriptionally. Thus, their expression levels are considered neutral, with respect to the CDK2 hypothesis. Indeed, we outline further studies below which could bear on the status of these proteins, and could potentially support or refute this hypothesis.

Second Hypothesis

V-src sarcoma viral oncogene homolog (SRC) a tyrosine kinase, is over-expressed in the tumor sample. SRC is a tyrosine kinase that is involved in several signaling pathways related to oncogenic processes and has been implicated in several cancers including melanoma. It plays a central role in modulating the ERK/MAPK pathway as shown below (FIG. 16), with gene expression and deletions overlaid. In this pathway, SRC potentiates the growth factor and/or integrin-mediated activation of RAS, with subsequent downstream activation of cell proliferation via the ERK/MAPK cascade (Bertotti, et al., 2006). There is a large body of literature establishing that the ERK/MAPK pathway is the major downstream effector of RAS dysregulation leading to oncogenic transformation. Thus, overexpression of SRC would be expected to amplify extracellular growth-related signals triggered by multiple growth factors or other ligands acting via receptor tyrosine kinases (RTKs), and thereby stimulate cell proliferation.

SRC is a molecular target of the drug dasatanib, which is a dual BCR-ABL kinase and Src family kinase inhibitor. It is currently approved for imatanib-resistant CML and treatment-resistant Ph+ ALL. It is also in clinical trials for metastatic melanoma.

Third Hypothesis

LCK, a member of the Src tyrosine kinase family, is normally expressed in T-lymphocytes and is a target of leukemia drugs. However, it has also been investigated as a melanoma target, and recently the inhibitor dasatanib (approved for use in CML and ALL) has been shown to induce cell cycle arrest and apoptosis, and inhibit migration and invasion of melanoma cells (Eustace, et al., 2008). The central role of LCK in the SAP/JNK pathway and the expression and deletion status of several other pathway members, indicating dysregulation of this pathway, is shown in FIG. 17. LCK activation may influence cell proliferation via activation of MEKK2, with subsequent activation of JNK. JNK has been shown to have both proliferation and pro-apoptotic effects depending on cell type and context.

For the SAP/JNK pathway, algorithm 1a gives probability of pattern being produced by chance (Π)=0.02.

The total pathway elements (q)=11, which are LCK, 1 upstream (TCR), 2 intermediaries before JNK (MEKK2, MKK4/7), JNK, 6 downstream of JNK (p53, AFT-2, Elk-1, c-Jun, NFAT4, NFATc1). The total aberrant genes consistent with hypothesis (n)=5, which are LCK, 1 upstream (TCR), 1 intermediaries before JNK (MEKK2), JNK, 1 downstream of JNK (c-Jun). The total number of possible pathways (N): Assume ˜200 based on XXX canonical pathways within Ingenuity and the cut-off probability (p)=0.05.

Concurrent inhibition of LCK and SRC might be expected to enhanced efficacy given the over-expression of both these targets in the tumor tissue, and their involvement in different but complementary pathways involved in oncogenesis and tumor maintenance. It should be noted, however, that because of its role in T-cell activation, inhibition of LCK could possibly have an immunosuppressant effect.

Our current technology collection is relatively insensitive to the types of measures that would evaluate immunotherapy as a potential recommendation. While we address biological questions about the tumor itself, more information either about systemic immune function, or specific populations of tumor-associated T-cells would be necessary to address this option. We are currently investigating the feasibility of adding this capability at a later date.

Additional Insight

We did however note a finding which, in hindsight, may be consistent with the patient's successful response to immunotherapy. In the tumor sample, the gene for Complement factor H (CFH) is deleted. CFH is a protein that regulates complement activation and restricts complement-mediated cytotoxicity to microbial infections. There is literature demonstrating that down-regulation of CFH in cancer cells sensitizes them to complement attack which can inhibit their growth in vitro and in vivo (Ajona, et al., 2007). To the extent that the patient's response to anti-CTLA4 therapy is related to a complement-mediated component of the immune response, the deletion of CFH might be a predictor of this vulnerability of the tumor.

Summary

Several dysregulated pathways with possible connections to melanoma were found: Cyclin-dependent kinase 2 (CDK2; inhibited by several drugs currently in development, including flavopiridol and CYC202), v-src sarcoma viral oncogene homolog (SRC), and lymphocyte-specific protein tyrosine kinase (LCK; both inhibited by the approved drug dasatanib). These targets all play roles in cancer-related signaling pathways, and have been specifically linked in the literature to melanoma progression. Additionally, deletion of the CFH gene, which can sensitize cells to complement attack, could possibly help explain the vulnerability of the tumor to immunotherapy.

Two drugs, flavopiridol and CYC202, targeting CDK2 are currently in clinical trials for melanoma and other cancers. Dasatanib, which targets both SRC and LCK, is approved for several leukemias, and is also in clinical trials for melanoma. Thus, while the first hypothesis (CDK2 pathway) is scientifically stronger, the key drugs that target the pathway are still in clinical trials. The pathways in the second two hypotheses (SRC and LCK) may be more easily targeted by an already approved drug.

The following steps are taken further to validate these hypotheses:

Elucidation of CDK2 pathway dysregulation at the protein level to determine the activation state of CDK2 and Rb as assessed by phosphorylation status. Thus, immunohistochemistry of tumor sections and control tissue using phospho-specific antibodies to CDK2 (Thr160) and Rb (Thr821), is performed as hypophosphorylation of CDK2 and hyperphosphorylation of Rb would support the validation of CDK2 as a target.

Elucidation of SRC pathway dysregulation at the protein level to determine if downstream effectors of SRC are hyper-activated. Thus, immunohistochemistry of tumor sections and control tissue using phospho-specific antibodies to ERK1/2, downstream effectors of SRC with respect to cell growth and proliferation, is performed as hyperphosphorylation of ERK1/2 indicates over-activation by SRC and supports the validation of SRC as a target.

Elucidation of LCK pathway dysregulation at the protein level to determine if downstream effectors of LCK are hyper-activated. Thus, immunohistochemistry of tumor sections and control tissue is performed using phospho-specific antibodies to JNK1/2/3, downstream effectors of LCK with respect to cell growth and proliferation, as hyperphosphorylation of JNK1/2/3 indicates over-activation by LCK and supports the validation of LCK as a target.

In vitro validation of the CDK2, SRC, and LCK hypotheses is performed using a cell line derived from tumor. Tumor cells from a fresh sample of tumor tissue are cultured and maintained in vitro, followed by treatment with CDK2 inhibitors, or dasatanib (SRC/LCK inhibitor), to assess anti-proliferative effects of these agents. Measured endpoints are cell proliferation (using, e.g., ATP charge) and apoptosis (using one of several readily available assays).

In vivo validation of the CDK2, SRC, and LCK hypotheses is performed using xenograft models, such as a mouse xenograft model derived from cultured tumor cells (described above). This model is used to test CDK2 inhibitors and dasatanib for anti-tumor effects, using change in tumor size as the measured endpoint.

These are the complete networks of interacting genes generated from the filtered list of up- and down-regulated genes. The Score column represents the overall level of interconnectedness within each network, and the Top Functions column describes cellular functions that are over-represented (relative to chance) in each network, as determined by the individual annotation of each gene in the network.

TABLE 4 Regulated Networks from Melanoma Tumor Sample Focus ID Molecules in Network Score Molecules Top Functions 1 ADPRH, C11ORF67, C19ORF42, C1ORF163, C4ORF19, C6ORF123, 32 35 Molecular Transport, C6ORF208, CCDC115, CRYZL1, DDX47, HBS1L, Cellular Development, HMGN4, HNF4A, INTS4, JTB, KIAA0409, MLF1IP, MRPL22, Lipid Metabolism MRPL53, MRPS35, MRPS18C, PPP2R3C, SLC26A1, SLC7A6OS, SNX11, SPATA6, STARD7, TAPBPL, TBC1D16, TIGD6, TMEM79, TMEM63A, TRIM4, TXNDC14, ZSCAN18 2 ADNP, ASNS, BRD3, C19ORF12, CALCRL, CDH11, CDH16, 32 35 Reproductive System CSH1, DYNC1H1, FADS3, FGF10, FMO2, GPR6, GPR56, Development and GTPBP4, HOXA7, HPGD, LMO7, LOXL2, MAP7, MSI2, Function, Gene NADSYN1, NOTCH2NL, PSG4, PSG9, PTPN12, RBM33, S100A13, Expression, Cancer SMARCA4, ST6GALNAC4, TBX5, TEX10, TMEM123, WWTR1, ZNF143 3 ANAPC1, BAT1, C3ORF26, CCNC, DCD, DDX52, DICER1, 32 35 Gene Expression, Cell DIMT1L, EXOC4, HDAC6, HNRNPM, HNRPLL, LMNA, Signaling, RNA Post- LSM11, MED6, MED12, MED13, MED16, MED17, MED22, Transcriptional MED25, MED31, MYBBP1A, NOC4L, NONO, PPIG, PTBP1, Modification RAB25, RBM39, RPS15A, SFRS3, SMC4, SNRPD1, TOR1AIP1, UBTF 4 C10ORF54, C11ORF58, CCND2, CIAPIN1, CREM, DAD1, 32 35 Gene Expression, DDT, DHRS1, EIF3M, ETFB, EWSR1, FAM49B, FBXL18, Cancer, Cell Cycle GLRX3, HDAC3, HPRT1, IKBKE, KIAA1683, MRPS18B, MTPN, NSUN4, PDIA6, PLSCR1, PPIB, PTRH2, RAB37, RHOB, RPL22, SPCS2, SPG7, TMEM111, TMSB4Y, TNFRSF14, ZNF638, ZNF764 5 ALOX15B, CRKRS, DDX17, ELOVL1, FIP1L1, FMN1, FNBP4, 29 34 RNA Post- FUS, HIST1H1C, HNRNPA1, HNRNPA2B1, KRT31, Transcriptional MYBPH, MYEF2, NES, P38 MAPK, PLEKHB2, PRPF40A, Modification, Lipid RNPS1, SF1, SFPQ, SFRS1, SFRS2, SFRS4, SFRS6, SFRS9, Metabolism, Small SFRS10, SFRS2IP, SNRPA1, SRPK1, TOP1, TWF2, U2AF1, Molecule Biochemistry ZFHX3, ZFR 6 ABI2, ADAM22, ANXA11, BCOR, BPTF, C17ORF59, CBLC, 29 34 Cellular Assembly and CCKAR, CEP290, CHMP7, CHMP4B, CHST12, CPNE4, Organization, Cellular HNRNPH3, Mapk, MAZ, NDE1, OLIG2, PCGF1, PCM1, PCNT, Development, Cellular PDCD6, PLSCR3, PTPN23, PTPRH, SH3KBP1, SMARCA1, Growth and Proliferation SNX13, SRI, TPM2, TRIB1, TUBGCP2, UGCGL1, UGCGL2, WISP1 7 APPBP2, C21ORF113, CRABP1, CUL4B, DDIT4, DECR1, 29 34 Gene Expression, DLGAP1, DSCR3, ESR1, GRWD1, HMGN1, IQWD1, JAG2, Cellular Assembly and LDLRAP1, LRP2, MFAP5, NR2C1, NR2C2, NR2C2AP, Organization, Lipid NRIP2, PRDM2, Rbp, RBP2, RBP7, RORB, SMU1, SORBS2, Metabolism SYNJ2, SYNJ2BP, TAF1B, TCF7, UMODL1, WDR26, WDR61, WWC1 8 BCCIP, C1ORF94, CCNA2, CCNB1, CCNB2, CCNF, CDC2, 29 34 Cell Cycle, Cancer, CDC25A, CDCA3, CDK2, CDKN3, CHML, Cyclin B, DNM1L, Reproductive System FOXM1, GOLGA2, GORASP1, GORASP2, KLK4, MAP4, Disease MXI1, PAPOLA, PSENEN, PTMA, RAB1A, RNF17, SKI, TMED2, TMED3, TMED10, UBE2A, UBR3, USO1, WEE1, ZNF593 9 ACVR1B, ALAD, CCT2, CCT4, CCT7, CCT8, DKC1, DLX1, 29 34 Carbohydrate EEF1G, FEN1, HARS, INHBC, INSR, KLF9, LZTS1, NARG1, Metabolism, Genetic NAT5, NOLA1, Pseudouridylate synthase, RAD1, RAD51AP1 Disorder, Hematological RAD9B, RPUSD2, SEPP1, SEZ6L2, SFRS5, SIX4, SMURF1, Disease SNX1, SSR1, TRUB1, TXNDC9, ULK2, VPS35, ZFAND5 10 AKNA, ASF1A, ATRX, C19ORF50, CBX5, CHAF1A, CYBB, 29 34 Cell Cycle, Cellular DNMT3A, EDN3, EXOC5, EXOC7, GIGYF2, Hdac, HOXA10, Assembly and ITGB3, KCNQ1OT1, LAMA4, MBD2, MOBKL1B, Organization, DNA MYO10, MYT1, NCF1, NR4A3, NSL1, PAX7, RBBP4, SNRPC, Replication, SNRPN, SRP9, SUZ12, TCEB3B, TLK1, TLK2, TRIM52, Recombination, and ZC4H2 Repair 11 APIP, ATG7, C1QBP, C1QTNF5, COIL, DCTD, DHX16, FAM115A, 29 34 Hematological System FAM176A, GIPC2, GSPT1, HERC3, IMPDH2, INO80C, Development and KPNA3, LNX1, MAP1LC3A, MGEA5, NUMBL, PAICS, Function, PIAS4, PREPL, Proteasome, PTN, PTPRZ1, RNF112, Hematopoiesis, SAT1, SDC3, SHMT2, SLC25A36, TAC3, TAF1D, TNFRS Organismal F10D, WFDC2, ZNF277 Development 12 AKR1C1, COPS6, COPS8, COPS7A, COPS7B, CUGBP1, CUL4A, 27 33 Protein Synthesis, Gene DIS3L2, DPYSL3, DYNLRB1, eIF, EIF5, EIF1AX, eIF2B, Expression, RNA EIF2B1, EIF2S1, EIF2S3, EIF3A, EIF3E, EIF3G, EIF3K, Trafficking EIF4A2, EIF4B, EIF4G3, EIF5B, HAPLN1, IGF1, IMPACT, MBNL1, MPHOSPH6, NCBP2, PARN, RPS2, SGK3, VPRBP 13 ACE2, Angiotensin II receptor type 1, ANGPT2, APLN, APLNR, 27 33 Cardiovascular Disease, CBFA2T2, CCL14, CRYBB3, GART, HEXIM1, HIF3A, Genetic Disorder, HSD17B14, HTATIP2, ICA1, Integrin alpha 2 beta 1, KLHL20, Cardiovascular System MELK, MKKS, NUP133, PTGER3, PTGS1, RASGRP3, Development and RPIA, SNRK, STARD8, STC1, STK16, SYNM, TBC1D8, Function TMSB10, TNPO2, VASH1, VEGFA, WDYHV1, ZNF124 14 ACP5, CCDC67, Ctbp, CYP17, DDX3X, DEFA4, FAM120C, 27 33 Endocrine System HNRNPU, IFIT3, IKZF1, IKZF4, LPCAT1, LRCH4, MC2R, Disorders, Genetic MRAP, NCOR2, NR6A1, NUFIP2, NUP50, OGDH, OGT, Disorder, Infectious PCSK1N, PEX5L, PML, POMC, RBAK, RREB1, SART1, SEH1L, Disease SNW1, TRAK1, TSR1, ZC3HAV1, ZNF462, ZNF687 15 ACE, ADRA2B, AGTR1, AGTR2, ANTXR2, BCLAF1, CLNS1A, 27 33 Cardiovascular System DRD2, FREQ, Gi-coupled Development and receptor, GRM8, HMGB1 (includes EG: 3146), HNRNPA3, Function, Cardiac HTR1D, IPO9, KCNAB1, KCND2, KCNE4, KCNIP2, KCNIP4, Arrhythmia, KCNJ1, KCNQ1, LRP4, MTUS1, NKTR, OPRM1, Pka, PPP1R9A, Cardiovascular Disease PPP1R9B, RPL19, RPS7, RPS19, SNPH, TNP2, WWP2 16 14-3-3(&beta;, &gamma;, &theta;, &eta;, &zeta;), AHI1, ASCL2, 27 33 Cell Morphology, BUB1B, CADM1, CALM3, CEP152, CRTAM, CTPS, Cellular Compromise, CTSD, EMP3, EPB41L3, FRMD6, HECTD1, Hsp90, ICK, LARP1, Molecular Transport LYST, MLXIP, NLRP1, NLRP3, P2RX7, PIH1D1, PLEKHF1, PPFIBP1, PRMT5, RPAP3, SLC4A7, SMYD2, SNCG, SSH1, SSH2, WDR77, YWHAB, YWHAG 17 ANPEP, ATP1B1, ATP1B3, BASP1, BCAM, BMP1, CCDC80, 27 33 Organismal CDH17, CDX2, CHRD, COL5A2, CTSLL3, Cyclin A, Cyclin Development, Cell E, DMP1, FAH, FOXC1, GZMK, HOXC10, KIAA1274, Morphology, Skeletal KRAS, LAMA2, LAMA5, LAMC2, LGALS3, MYBL2, NRN1, and Muscular System PBX2, RAB20, TBX1, TFDP1, TLL2, TPM3, VAPA, YLPM1 Development and Function 18 ABCC1, ACHE, ACLY, ARNTL, BHLHB3, CLMN, DAO, ELA2, 27 33 Cellular Compromise, ENPP2, FOSL2, FOXA2, FXR1, GABPA, GCK, GMFB, Lipid Metabolism, GPAM, GPR146, HECW2, Hexokinase, HN1, IER3, MYOD1, Molecular Transport NAP1L3, NOV, ONECUT2, PROC, SDC1, Sod, SOD1, SOD2, STAB2, TAT, THBS1, TMC1, TP73 19 Adaptor protein 1, AP1G1, AP1GBP1, AP1S1, AP1S2, ARCN1, 25 32 Cellular Assembly and ARMC6, BICD1, COP I, COPB1, COPB2, COPE, COPG2, Organization, Cell CPE, CXCL14, DNASE1, GGA1, GGA3, GUSB, Hdac1/2, Morphology, Cell-To- IL8, ING1, KIF13A, LAT2, LPAR3, M6PR, PACS2, PIGR, POLDIP2, Cell Signaling and PTGER1, SAP30, SCAMP1, STK36, TFF3, VANGL1 Interaction 20 ADARB1, AKR7A2, BAT2, CCNO, CDC2L2, Cpsf, CPSF1, 25 32 RNA Post- CPSF2, CTH, DCBLD2, ELOVL2, GREM1, GTF2I, HDAC10, Transcriptional HMG CoA synthase, HMGCS1, HSD3B7, IARS, MAGEA6, Modification, Cell NUDT21, OMD, PDGF BB, PSMF1, QARS, RPL13, SBDS, Death, Embryonic SFRS7, SNTG1, SPTBN5, SRPRB, SSR3, SYMPK, SYNE1, Development TRIOBP, VAPB 21 AMH, CHM, CXCL2, ERC1, GDF9, LDL, MYO5A, NPC2, 25 32 Cellular Assembly and OSBPL6, PON2, PPFIA4, PRPS1, PTP4A2, Rab5, RAB22A, Organization, Hair and RAB27A, RAB27B, RAB4A, RAB6A, RABAC1, RABGGTB, Skin Development and Ribose-phosphate diphosphokinase, RPH3A, SNCB, SPTG21, Function, Organ ST14, STAR, SYP, SYTL2, SYTL5, TNFAIP6, TRIM2, Morphology TRIM3, UBE2K, ZFYVE20 22 ABCB6, ANKHD1, APOD, ARAP1, ASXL1, BAZ2A, C5ORF34, 25 32 Embryonic CBX2, CRK/CRKL, DOCK1, DOT1L, DYRK3, EHMT1, Development, Tissue ELP4, GRINL1A, Histone h3, Histone-lysine N-methyltransferase, Development, Gene HOXC5, JMJD6, MLL3, PARP10, PCGF2, PHC2, Expression PHC3, RNF2, RPS9, RYBP, SCMH1, SRRM2, SYNE2, TFCP2, TM4SF18, TMEM70, XIST, ZDHHC11 23 ATM, Basc, BCL11A, CDYL, COL19A1, COL9A2, COL9A3, 25 32 DNA Replication, DUSP26, EHF, EIF2AK3, FBXL11, GINS2, HDAC2, HDAC4, Recombination, and HDAC9, HLA-DRA, HMGB3, HSF4, Hsp70, LIG4, MDC1, Repair, Cellular MED23, Mre11, MRE11A, NBN, PDS5A, POT1, SMC3, Development, SMC1A, TERF1, TFDP2, TP53BP1, WAPAL, XRCC5, Hematological System YY1 Development and Function 24 ADCY10, Ant, ATF7IP, CBR1, CDT1, CKMT1B, DBN1, DTL, 25 32 Cellular Assembly and ERCC8, GTF2H2, GTF2H3, GUCA1A, GUCY, hCG, HTRA2, Organization, Nucleic KIF5B, NIPBL, NPR1, NPR2, ODF2L, OPA1, PNOC, Acid Metabolism, Small PPID, PRSS23, S100B, SLC25A4, SLC25A6, SLC25A13, SLC4A4, Molecule Biochemistry SVEP1, TMEM158, TRAPPC4, TTC3, VDAC1, ZNF518A 25 AURKA, AURKB, BTRC, CAPRIN1, CELSR2, CKAP5, CSTF3, 25 33 Cancer, Renal and DTYMK, E3 co-factor, FZR1, G3BP1, ID4, IDI1, KLF6, Urological Disease, KPNA1, KRT4, LRRFIP1, PFKL, PFKM, PINK1, PTEN, PTPRN2, Nucleic Acid RAB10, RNASEH1, RRM1, RRM2, Scf, SEL1L, SERPINH1, Metabolism SKP2, SMOX, SORD, TACC1, TCF12, TNFAIP1 26 ANLN, ARL6IP1, ASPM, ATAD2, ATM/ATR, BLZF1, BRCC3, 24 34 Cancer, Cell Cycle, C14ORF106, CDC14A, CKAP2, CPOX, CTSF, EEF1E1, Gastrointestinal Disease FIGNL1, FXYD3, HUWE1, MAGEA2, NUP85, PIGF, PIGG, PPA1, PPP4R2, PRKRIR, RCHY1, SLC19A2, SLC6A6, SNAPC5, TCN2, TEAD2, TFAM, TMEM97, TNFRSF10C, TP53, TULP4, UBA6 27 ADAP1, Adaptor protein 2, AGXT, AHNAK, APOBEC3C, 24 32 Neurological Disease, CACNB2, CACYBP, CNBP, GABAR-A, GABRA5, GABRB3, Developmental Disorder, GABRE, GABRG1, GABRG3, GABRP, GABRQ, GABRR1, Psychological Disorders GNE, GPHN, HNRNPAB, HNRNPR, IGF2BP1, KIAA1549, LAMP1, P2RX2, P2RX6, Pkc(s), PRKRA, RIF1, RPL35A, S100A12, SYNCRIP, SYT3, SYT7, TBL1X 28 ARIH1, CARD14, CDH22, DOK5, EDA, ETS, ICOSLG, IL1RL2, 24 31 Humoral Immune LINGO1, LTB, Lymphotoxin-alpha 1/beta 2, NFkB, NOL14, Response, Lymphoid PLEC1, POU2AF1, RAB3C, RBCK1, RNF216, RNF144B, Tissue Structure and RNF19B, SDS, SIAH2, SLC11A2, SLC37A4, SPIB, STK10, Development, Post- TNFRSF19, TNFRSF13C, TRIM9, UBE2, UBE2C, UBE2L6, Translational UBE2V1, WNT6, WNT10A Modification 29 AP4M1, AXIN2, BBX, Bcl9-Cbp/p300- 24 31 Cell-mediated Immune Ctnnb1-Lef/Tcf, CBFB, CCL18, CD6, CD58, CD3EAP, CLEC7A, Response, Hematological DEF6, ELAVL4, EOMES, Groucho, HIPK2, IL4, LAMP2, System Development LEF1, LEF/TCF, Mhc ii, MVK, NFATC2IP, NKX3-1, PKIB, and Function, Immune PTCH1, RUNX3, SLC26A2, SPDEF, SYT11, TBC1D17, Cell Trafficking TCF7L1, TLE4, TMEM131, TRA@, VTCN1 30 ACAP1, CCL19, CCL21, CDC16, COL8A1, CSF2, Csf2ra-Csf2rb, 24 31 Antigen Presentation, CSF2RB, CUL3, DDX5, DDX54, EP400, EPC1, ESR2, Cellular Movement, Esr1-Esr1-estrogen-estrogen, ESRRB, EXOSC3, HNRNPD, Hematological System HOOK1, ILF3, ING3, IRAK3, KHSRP, KLHL12, MAD2L1, Development and NACC1, NR0B1, PRMT2, Rab11, RBM17, SNX20, TIA1, TIAL1, Function TRAP/Media, TRRAP 31 APLP2, C7ORF64, C9ORF100, CNTN1, DAZ4, DZIP1, ERK1/ 24 31 Genetic Disorder, 2, GLYAT, HN1L, ID2, KIAA0182, LRRC41, MFNG, Notch, Neurological Disease, NOTCH2, NOVA2, NRCAM, PDLIM4, POU2F2, PSEN2, Molecular Transport PTBP2, QKI, RBPMS, RCOR3, Secretase gamma, SLC5A7, Smad1/5/8, SMUG1, STRBP, TIE1, TNR, USH1C, USH1G, XRCC6, XRCC6BP1 32 ANKRD44, BAP1, Cbp/p300, CYP19A1, DUB, ENO1, GATA4, 24 31 Organ Development, LHX9, MUC4, NR5A1, SMAD2, Sox, SOX1, SOX3, SOX11, Reproductive System SOX12, SOX15, TBC1D1, TBX18, UCHL1, UCHL5, Development and Unspecific monooxygenase, USP3, USP6, USP10, USP14, USP25, Function, Cancer USP31, USP32, USP42, USP46, USP48, USP53, ZNF653, ZNHIT6 33 CLN6, COL1A1, FLT1, FLT4, GIGYF1, GTF3A, IFI6, IFNGR2, 23 31 Cellular Movement, IL20, IL23, IL12RB1, IL13RA2, IL31RA, IL8RB, NPY2R, Cell-To-Cell Signaling NRP1, NRP2, OMP, PAIP2, PEG10, PLXNA3, PRELP, RGS12, and Interaction, Cardiac Sema3, SEMA3B, SEMA3G, SOCS7, STAT3, TMF1, Hemorrhaging TMPRSS6, Vegf, Vegf Receptor, ZBTB10, ZNF197, ZNF467 34 ABCC9, Ahr-aryl hydrocarbon- 22 31 Carbohydrate Arnt, ALDOA, B4GALT5, CSE1L, EEF2, Esr1- Metabolism, Gene Estrogen-Sp1, FAM120A, HSP90AB1, HSPH1, KLF15, L-l Expression, actate dehydrogenase, LDHA, LDHB, LDHC, MIER1, NFIA, Cardiovascular System NFIC, Nuclear factor 1, NUMA1, PKLR, PKM2, POGZ, PSAT1, Development and RPL5, RPL7, RPS3A, RPSA, SFTPC, SFXN3, SLC11A1, Function SP1, TRIP12, XPO1, ZCCHC14 35 ARID1A, ATF7, BATF, BATF3, BAZ1B, CEBPE, CSTA, CYSLTR1, 22 30 Gene Expression, CYSLTR2, EPB49, EVI1, FTH1, GATA1, GC-GCR Cellular Growth and dimer, HHEX, HIVEP3, Jnk dimer, JUN, JUN/JUNB/JUND, Proliferation, JUND, MAFF, MLLT6, MT2A, MTF1, NFE2L1, NRAP, PRRX1, Hematological System PXDN, SMARCA2, SMARCC1, SMARCD1, SRGAP3, Development and SWI-SNF, Tcf 1/3/4, TMC8 Function 36 ATP5B, CKAP4, CTNNA1, DNAH1, DNAJ, DNAJA1, DNAJA2, 22 30 Post-Translational DNAJB11, DNAJC, DNAJC4, DNAJC10, DNAJC5G, Modification, Protein G&alpha; q, GOT2, GPRIN2, HSP, Hsp22/Hsp40/Hsp90, Folding, Cellular HSP90AA1, HSPA5, HSPA8, HSPA9, KPNB1, KRT17, MAP3K7IP1, Function and NFE2L2, PITPNM3, PTK2B, SFN, SIL1, SYNPO2, Maintenance TNPO1, TRIM29, TUBB, TXN, XPO7 37 14-3-3, ARHGEF16, C12ORF51, CBLL1, CBY1, CD74, Cofilin, 22 30 Cancer, Reproductive FAM62A, HAT1, HLA-DPB1, HLA-DRB4, HMG20B, System Disease, KIAA1429, MDM4, MHC II-&beta, Mhc2 Alpha, MYST3, Neurological Disease MYST4, PABPN1, PANK2, PAPOLG, REEP6, RPL4, RPLP2, SH3BP4, SSBP1, STRAP, SUPT6H, TACC2, TPI1, TUBA4A, TUBB1, TUBB4, Tubulin, YWHAZ 38 ADA, BIN1, C4A, DDX1, DMC1, Dynamin, E2f, ENG, FKBP3, 22 30 Cell Cycle, DNA FMNL1, FOLR1, GOLT1B, HMOX1, IPO5, MCM5, MND1, Replication, NADPH oxidase, OBSL1, PFN1, Pld, PLD1, PRDX6, RAD51, Recombination, and RAD51C, RAD51L1, RALA, RANBP2, RIN3, RPS16, Repair, Cell Morphology SERBP1, SNCA, SYN2, TMEM126B, TYMS, tyrosine kinase 39 ACP1, ADAM12, AFAP1L2, ANKRD11, CRLF2, DDR2, DGKA, 20 29 Cell Morphology, Cell- DIAPH3, EPHA4, EPHA/B, Ephb, EPHB2, Ephb dimer, To-Cell Signaling and EPOR dimer, esr1/esr2, GPR172B, KBTBD2, KCNQ4, KDELR1, Interaction, Cellular NCK, NCKIPSD, NEU2, PIP, PTPN13, PTPN21, SDC2, Assembly and SERINC3, SH2D3C, SH3PXD2A, SKAP2, SLC16A1, Organization SRC, TNS1, WBP5, WWOX 40 ALK, ARHGEF15, BRAP, BRD7, C11ORF49, CENPF, CHKA, 20 29 Cancer, Cell Cycle, Cell CTBP2, EFNA5, ENaC, EPHA, EPHA1, EPHA10, EPHB4, Morphology H19, IGF2BP3, IRS, ITPR3, KRT74, MAPK8IP3, MARK1, MPRIP, NEFL, Pdgfra-Pdgfrb, PDLIM5, PI3K, PIK3AP1, PIK3R2, RAP1B, RGL3, Rock, SCN7A, SCNN1B, SHC3, SLC12A4 41 ADRA1B, ARMC2, CHMP4C, CLU, CP, Cyclooxygenase, FPR1, 20 29 Cancer, Reproductive FPR2, GJC2, HDL, HMGA2, HPSE, IGHG1, IGKC, IL1, System Disease, Free Interferon Radical Scavenging beta, KIAA1409, KLK3, KRT7, KRT14, KRT16, MOCOS, Neurotrophin, PAPPA, PKHD1, PNMA2, SAA1, SAA4, SAA@, SCAF1, SLC2A3, SORCS1, SORT1, SPEF2, TTPAL 42 ATF6, BEX2, CALR, CCR4, CD8, CREBL1, ERO1L, GATA3, 20 29 Hematological Disease, GOT, GZMA, HIST1H1T, HLA- Immunological Disease, E, HMGB2, HSP90B1, Ige, IL12, IL33, IL1RAP, IL1RL1, ITM2A, Infectious Disease LMO2, MHC Class I, NASP, NHLH2, P4HB, PDIA2, PDIA3, PDIA4, PHOX2A, POU3F1, SET, TAL2, Tap, TNFRSF25, TTLL5 43 ACTR2, ACTR3, ACTR1A, ACTR3B, AIF1, Arp2/3, ARPC5, 20 29 Cell-To-Cell Signaling CDC42EP4, CFL2, CIAO1, CLCN3, CYFIP1, DIXDC1, FCGR1A/ and Interaction, Cellular 2A/3A, G-Actin, GIT2, GNL3, GOLM1, IQUB, KIAA1967, Assembly and MYLK3, MYO1D, NCKAP1, NWASP, PAK1, PCDH7, Organization, Skeletal PGAM1, Rac, RPL13A, TMEM33, TNFSF11, TROVE2, and Muscular System WAS, WASP, WIPF1 Development and Function 44 adenylate kinase, AK2, AK7, AK3L1, Alcohol group accept 20 29 Amino Acid or phosphotransferase, ATYPICAL PROTEIN KINASE C, Metabolism, Post- CDK6, CDK4/6, COQ7, DMPK, DYRK1A, FBXO7, HIPK1, Translational HSPE1, IRAK1, MAP2K2, MAP2K5, Map3k, MAP3K7, MAPK6, Modification, Small MAPK11, PAK2, PDK1, PDPK1, PKN2, PLK3, PPT1, Molecule Biochemistry PRKCH, PRPF4B, RPL29, Rsk, RTCD1, SH3RF3, TOMM70A, WDR68 45 3 BETA HSD, BRF2, C2ORF47, CLEC11A, DCP2, EML4, 20 29 Free Radical Fcer1, HIGD1A, HSD17B11, LRPPRC, Mek, MTR, NADH Scavenging, Genetic dehydrogenase, NADH2 dehydrogenase, NADH2 Disorder, Neurological dehydrogenase Disease (ubiquinone), NDUFA1, NDUFA2, NDUFA4, NDUFA7, NDUFA8, NDUFA12, NDUFA13, NDUFB2, NDUFB9, NDUFB11, NDUFS1, NDUFS3, NDUFS8, NEK6, OTUD4, PTPLAD1, SFXN4, STARD9, TSC22D1, UHRF1BP1 46 Alpha Actinin, BDNF, Calpain, CAPN6, CAPN10, CD37, CHRNA5, 20 29 Cellular Assembly and CHRNB2, Clathrin protein, CLTB, CTSS, CXCL10, Organization, Cellular EPN1, EPS15, EPS15L1, GAD, HLA-DQB1, JARID2, LBP, Function and LDB3, MBP, MHC Class II, MYOZ2, MYOZ3, Nicotinic acetylcholine Maintenance, Cellular receptor, NTRK2, PEG3, PICALM, PTPN1, RFXAP, Movement RPS21, SCN5A, SCT, SNAP91, VIPR1 47 ADCY, ADRA1A, ADRB2, ADRB3, Beta Arrestin, BRS3, 20 29 Cell Signaling, Nucleic C18ORF25, CFTR, CRHR1, CXCR4, DIO2, DZIP3, Galphai, Acid Metabolism, Small G-protein beta, GNA11, GNA12, GNA13, GNAQ, Molecule Biochemistry GNAS, GNB1, GNB1L, Gs-coupled receptor, HAX1, HGS, HTR6, KCNK1, PLC, PLCD3, PLEKHA6, UBE2E1, UBE2G1, UBE2I, UBE2J1, WSB1, XPNPEP3 48 C19ORF2, CCNT2, CDK9, DNA-directed DNA 20 31 DNA Replication, polymerase, DNA-directed RNA polymerase, E3 RING, Recombination, and HLA-DQA1, HTATSF1, MDM2, MYCN, NCL, PCNA, POLE, Repair, Gene Expression, POLE4, POLH, POLR1D, POLR2E, POLR2J2, POLR2K, Cell Cycle POLR3C, POLR3E, POLR3G, POLR3H, PRIM2, RAD18, RAD52, REV1, RFC3, RPA, RPL10, RPL37, RPS8, SSB, STK19, TFAP4 49 ALP, ALPI, ALPL, ARHGEF5, BCL2L11, Bmpr1, BMPR2, 20 29 Carbohydrate CAP2, CASP2, CRYM, DLX2, DLX4, DUSP14, FOXG1, FOXL1, Metabolism, Lipid Foxo, FSH, GAD1, GPRC5B, KRT84, LHX5, LMX1A, Metabolism, Small MAGED1, MSX2, OTP, PGRMC1, Pi4k, PI4K2A, PI4KA, Rb, Molecule Biochemistry SOX2, TCF3, TOX, TP53I11, UNC5A 50 ARHGAP4, ARHGAP6, ARHGAP8, ARHGAP29, ARHGDIA, 19 29 Cell Signaling, Cell ARPC2, BAIAP2, CDC42, COL10A1, COL11A2, DGKQ, Morphology, Cellular DIAPH1, Erm, G&alpha; 12/13, GRLF1, HPCA, IBSP, ICMT, Assembly and KIFAP3, MCF2L, MSN, MYO9B, Phosphatidylinositol4, Organization 5 kinase, PIP5K1B, Ras homolog, RDX, RGMA, RHOA, RhoGap, RHOH, RHOJ, SPN, srGAP, SRGAP1, SRGAP2 51 ATG5, ATXN3, CTSE, EMG1, Immunoproteasome Pa28/20s, 19 29 Cellular Assembly and KHDRBS1, LCK, MAGEA3, NAT6, PMS2, Proteasome Organization, Cellular PA700/20s, PSD2, PSMA, PSMA1, PSMA5, PSMB, PSMB2, Function and PSMB6, PSMB7, PSMB8, PSMC, PSMC1, PSMC2, PSMC4, Maintenance, Connective PSMD, PSMD4, PSMD7, PSMD14, PTPN22, SNCAIP, SNX3, Tissue Disorders SOS2, WDR48, ZDHHC17, ZDHHC18 52 ACAC, ACACB, ACAT1, ADIPOQ, ADIPOR2, AMPK, Creatine 19 28 Lipid Metabolism, Small Kinase, Cytochrome c, ELOVL6, ENSA, FABP, FABP2, Molecule Biochemistry, FABP3, FABP6, FOXO3, GYS2, HMGCR, Insulin, KHK, Carbohydrate LCP2, LIPE, MLXIPL, PLB1, POLDIP3, PRKAA, PRKAA2, Metabolism PRKAB1, PRKAB2, PRKAG1, SLC25A12, STX6, STXBP4, UCP3, UQCRC2, UQCRH 53 AHSA1, ANAPC10, ANP32A, CALU, CAMK2D, Glycogen 19 28 Cancer, Cell Death, synthase, HSPD1, MAP2K1/2, MID1, Myosin light chain, Reproductive System NEK1, NPTX2, PFKFB2, PP1, PP1/PP2A, PP2A, PPP1CB, PPP1R11, Disease PPP1R1A, PPP2R1A, PPP2R2A, PPP2R2C, PPP2R5A, PPP2R5B, PPP2R5C, PSME2, Pyruvate kinase, RAB18, RAB11A, RCN1, RCN2, SLC6A4, SLC8A1, TMEM43, UPF1 54 ABCB4, ABCG4, AP4E1, APOA4, APOC4, ARG2, CD36, CNOT1, 19 28 Lipid Metabolism, CNOT4, Coup-Tf, CPT1, CPT1B, CTNNBL1, CYP24A1, Molecular Transport, FXR ligand-FXR-Retinoic acid-RXR&alpha, Small Molecule GCN1L1, GPLD1, INSIG2, LTF, N-cor, NCOR-LXR- Biochemistry Oxysterol-RXR-9 cis RA, Nr1h, NR1H4, NR2F2, NUDT7, PALB2, RANBP3, RARA, RQCD1, Rxr, SGOL2, SPP1, THRB, UCP1, USF2 55 AGMAT, Arginase, BAT3, BIRC5, Caspase, DPT, DTX3L, FGF20, 19 28 Cell Death, Metabolic FGFR3, IFT57, Ikb, JUP, KHDC1, KLF5, MAPKAP1, Disease, Genetic MST1, NGLY1, Nos, OTUD7B, PCCA, peptidase, PTPN14, Disorder RNF7, RNF130, SHFM1, STAM2, TH1L, TMPRSS11D, Tnf receptor, TTLL3, UBC, UBE2D3, Ubiquitin, UBL7, ZNRF1 56 CD44, CSF1, CSPG4, DCN, ELN, FBN1, FCN1, FCN2, Fgf, Fibrin, 18 28 Protein Degradation, GDF15, Gpcr, GPR68, Igfbp, KISS1, Mmp, MMP2, MMP7, Connective Tissue MMP12, MMP14, MMP15, MMP16, MMP20, MMP24, Disorders, Genetic NUAK1, PCDHGC3, PCOLCE, PCSK6, Plasminogen Disorder Activator, PLAU, PTPRO, SERPINE2, SPOCK3, Trypsin, VCAN 57 ALS2CR11, CD27, CD70, CNKSR1, DUSP4, DUSP6, DUSP9, 17 27 Cell Signaling, DUSP16, E4F1, ELK4, GCKs, Il3r, Jnk, Jnkk, KIAA1217, Embryonic MAP1S, MAP3K2, MAP3K4, MAP4K2, MAP4K5, MAPK10, Development, Tissue MAPK12, MEKK, MEKKs, MGAT3, MINK1, MKP1/2/ Development 3/4, NET1, PRDX4, RASSF1, RHPN1, SERPINB3, TAOK3, TEX11, TRAF 58 3′,5′-cyclic-nucleotide phosphodiesterase, AKAP, AKAP1, 17 27 Cardiovascular Disease, AKAP5, AKAP10, Ap1, ARFGEF2, ARL3, C2ORF65, C3ORF15, Visual System Camk, CBFA2T3, CRX, ELL, GSS, NPHS2, NRL, OSGIN1, Development and PADI4, Pde, PDE11A, PDE4A, PDE4D, PDE4DIP, PDE6 Function, Kidney Failure (rod), PDE6B, PDE6D, PDE6G, PDZK1, Pki, PRKAC, PRKAR2A, PRKAR2B, PTGIR, SIRT2 59 CARS, CBX3, CDX4, CREB1, FUSIP1, GALNT1, GALNT2, 17 27 Post-Translational GALNT5, GALNT7, GALNT8, Glutathione peroxidase, Modification, Gene GTF2A2, GTF2E1, GTF2F1, HPS6, MUC1, PDHA2, PEPCK, Expression, Cellular Polypeptide N-acetylgalactosaminyltransferase, RNA Compromise polymerase II, SLC18A2, SUB1, Taf, TAF1, TAF5, TAF11, TAF1L, TAF9B, TFIIA, TFIIE, TFIIH, TGS1, UPP1, VSNL1, ZEB1 60 BRAF, CD3, CD247, CFLAR, CRKL, DOCK2, DST, Dynein, 17 27 Nervous System FKBP4, FLT3, GLMN, GRIA2, GRIA3, IKK, IL2RA, JINK1/ Development and 2, MAP1B, MAP4K4, NF2, NUDCD3, OIP5, PAFAH1B1, PCDHA6, Function, Cancer, POP7, PPIL2, Ptk, Rar, RELN, RPP30, SERPINB10, Neurological Disease SIRPA, SPIC, STAT5a/b, TCR, TUBB2B 61 ABCC3, ABCC4, ALDH, CAR ligand-CAR- 17 27 Drug Metabolism, Retinoic acid-RXR&alpha, CES3, CITED2, CNOT3, CREBBP, Vitamin and Mineral CRYAA, CRYGC, CYP2C9, EID1, GST, HIST1H4E, HOXD10, Metabolism, Amino HS3ST1, HS3ST3A1, HS3ST3B1, MAST1, MIP, NCOA, Acid Metabolism Ncoa-Nr1i3-Rxra, NR1I3, PXR ligand-PXR-Retinoic acid-RXR&alpha;, Retinoic acid- RAR-RXR, SCAND1, SLC35A2, SS18L1, sulfotransferase, SULT1A3, SULT1E1, SULT4A1, ZNF434, ZNF446, ZSCAN20 62 AGK, BDKRB2, C16ORF14, CASD1, CD48, CHRM2, DOK4, 17 28 Cell Signaling, Nucleic ERK, FAU, G alpha, G alpha-G beta-GDP-G gamma, G Acid Metabolism, Small protein beta gamma, G-protein gamma, GNA14, GNAI1, Molecule Biochemistry GNAL, GNG2, GNG4, GNG7, GNG12, GPER, GPR1, GPR4, KCNJ5, KLB, MPZL1, Plc beta, S1PR, S1PR2, S1PR3, S1PR5, SPRED1, TFG, TNFSF8, UBA5 63 BGN, BMP, BTF3, CDKN2B, CK1/2, COL1A2, CSNK1A1, 17 28 Gene Expression, CSNK1E, CSNK2A1, CSNK2A2, EGLN1, EID2, ENTPD5, Cancer, Gastrointestinal FST, Glucocorticoid-GCR, JPH3, NAP1L1, PEX6, PURB, Disease RFX2, RFX3, RNF111, Smad, SMAD3, SMAD4, SMAD5, Smad2/3, Smad2/3-Smad4, SSRP1, SUPT16H, TEAD1, TFE3, Tgf beta, TGFB2, YBX1 64 ATP2A1, ATP2A3, ATP2B2, C16ORF70, Ca2 16 26 Cell-To-Cell Signaling ATPase, Calmodulin, CASK, CASQ2, CTNNA3, DLG1, F11R, and Interaction, Cellular GPR124, Guk, HR, JAM, JAM3, KCNJ10, MLLT4, MPP4, Assembly and MPP5, MPP7, PARD3, Pmca, PVRL4, RGN, Ryr, SERCA, SIPA1, Organization, Cellular SLC16A3, SSX2IP, T3-TR-RXR, TBR1, THRA, Thyroid Function and hormone receptor, TRDN Maintenance 65 ADAM9, ADAM10, ADAM28, ADAM30, C5ORF13, CD9, 16 27 Cancer, Cell-To-Cell CD53, COL13A1, F12, FN1, FYB, GAL3ST1, HTRA1, I Signaling and kappa b kinase, ICAM2, Integrin, Integrin alpha V beta Interaction, 3, Integrin&alpha;, Integrin&beta;, ITGA4, ITGA9, ITGAE, Hematological Disease ITGB1, ITGB5, ITGB8, LACRT, LTBP1, LTBP3, Metalloprotease, MIA, PACSIN3, PRAP1, Talin, VTN, WASP- SLAP130-SLP76-VASP-NCK-VAV 66 Akt, CDH13, CEP55, CISH, COL4A3BP, CTF1, FKHR, IL11, 16 26 Cellular Growth and IL6ST, INPP4A, IRS2, JAK, JAK1, LEPR, LIFR, LMO4, LPXN, Proliferation, MPL, PHIP, POM121, SCGB3A1, SEC14L2, SOCS, SOCS3, Hematological System SOCS4, SOCS6, STAT, STAT1/3/5 dimer, Stat3- Development and Stat3, TBC1D4, TPOR Function, Inflammatory dimer, UXS1, VPS37A, VPS37B, WSX1-gp130 Response 67 APOBEC3G, AZI2, BFAR, CASP1, CASP10, CD86, CELSR1, 16 26 Gene Expression, CYB561, CYLD, IFIH1, IFN ALPHA RECEPTOR, IFN Antigen Presentation, Beta, Ifn gamma, IFNA4, IFNAR1, Interferon Antimicrobial Response alpha, IRF, IRF8, ISGF3, NF- &kappa; B, Oas, OAS2, PECAM1, PTGDR, RARRES3, RSAD2, SF3A1, SGSM3, SLC2A11, TANK, TGM1, Tlr, TNFAIP3, TRIB2, ZBP1 68 AMD1, BLK, BRSK1, C2, C1q, C1R, CARD8, CARD16, CD22, 15 27 Cell Death, CD300A, CD79A, Complement component Hematological Disease, 1, DLEU2, ENPP3, FCAR, Fcgr2, FCGR2A, HRG, IgG, IGHE, Immunological Disease IGHM, IGL@, Igm, IL21, INPP5D, LILRB1, MS4A1, NUP205, PEAR1, SHC1, SHCBP1, SHIP, SHP, SYK/ZAP, TPR 69 ARVCF, CABP1, CaMK-II/IV, CAMK2A, CAMK2G, CaMKII, 15 25 Neurological Disease, CDH2, CHRNE, CK1, Creb, EGLN3, Filamin, GPD1, GRIN, Organismal Injury and GRIN2A, GRIN2C, GRIN3B, LRRC7, MUC5AC, MYOG, Abnormalities, NMDA Receptor, NOX5, PACSIN2, PYGM, SDHD, SIX2, Respiratory Disease SLC32A1, Succinate dehydrogenase, Top2, Wnt, WNT3, WNT11, WNT5A, WNT8B, WNT9A 70 ANKRD10, BARHL1, BAT1, C12ORF41, C3ORF26, CIRH1A, 15 25 Gene Expression, CLUAP1, CNIH, DDX52, DHX15, DIMT1L, INTS2, MAGEA11, Auditory Disease, MBOAT2, MED13, MED23, MED28, MED7 Cellular Compromise (includes EG: 9443), MIRN222 (includes EG: 407007), MYBBP1A, PNN, POLR1D, POLR2C, POLR3A, PPIG, PRKRIP1, PRPF4B, PSMC6, SFRS3, SMC4, SPRYD5, TCOF1, TFIP11, TRMT1, WTAP 71 Actin, Alpha actin, ATPase, CALD1, CASC1, CCL5, CCL16, 14 24 Cardiovascular System CD163, CEACAM1, Ck2, CTSB, CXCR5, DTNA, F Actin, Development and Mlc, MYH3, MYH7, MYH8, MYH14, MYL6, Myosin, Myosin Function, Skeletal and Light Chain Kinase, NAMPT, NFKBIL2, PRKG1, Muscular System SERPINB13, SQLE, STK17A, Tni, TNNI3, TNNT2, Tropomyosin, Development and Troponin t, TUBB2A, TUBB2C Function, Tissue Development 72 AKT3, C16ORF35, C5ORF13, CAPN6, CCNT2, FAM38B, 14 24 Nervous System FBN2 (includes EG: 2201), FNDC3A, GSC2, JMJD1A, Development and KY, LGI2, LOXL3, MECP2, MIRNLET7A3, MIRNLET7F2 Function, Tissue (includes EG: 406889), NNT, NRK, PCDH7, PTPN12, Development, Cancer RALGPS2, RET, RNF4, SCARA3, SDK1, SLC2A3, SMARCA1, SNX25, TCIRG1, UBE2B, UBE2N, UBE2V2, UGCGL1, XPO4, ZNF215 73 AFAP1L1, AK5, C17ORF28, C7ORF26, CDK9, CEP57, COL5A2, 14 24 Cell Cycle, Connective E2F3, E2F4, E2F6, FRMD6, GCSH, H2AFX, HMGB1 Tissue Development and (includes EG: 3146), KHDRBS1, MAGT1, MYCN, NCL, Function, Gene NMI, NUCKS1, PPIL5, RABIF, RBBP8 (includes Expression EG: 5932), RBL2, RFC3, RNF114, RPL22, RPS16, RPS23, RUSC2, SRPR, TERC (includes EG: 7012), TERT, TRIM46, UBASH3B 74 ABCC1, ARSA, ARSI, ATP6V0B, BTBD7, C16ORF84, C17ORF39, 14 24 Lipid Metabolism, Small C2CD2L, C5ORF25, CCDC136, CEND1, CSPG4, Molecule Biochemistry, DCUN1D5, EMG1, EPC1, FABP3, FAM131A, IGSF9B, ILVBL, Carbohydrate IRF8, JMJD3, LARP4, MIRN337, MIRN130B Metabolism (includes EG: 406920), MIRN149 (includes EG: 406941), MIRN205 (includes EG: 406988), MIRN28 (includes EG: 407020), MUS81, RSF1, SAMD1, SFRS1, STBD1, SULF2, SUMF1, WDR44 75 ARL17P1, C15ORF15, C20ORF43, CDK5RAP3 (includes 13 18 Gene Expression, Lipid EG: 80279), CHERP, DNAJC30, GPR39, HNF1A, HNF4A, Metabolism, Molecular HOOK3, KIF9, MPZL2, MRPS23, OSBPL11, PRKAB1, retinoic Transport acid, SLC5A3, STX17, TTC25, TTC37, UFM1 76 B9D2, CNTN4, DCLK3, FBXO42, H2AFJ, HNF4A, INTS7, 13 17 Gene Expression, Amino MLL5, MSRA, NAALADL2, NFYB, NFYC, PAQR5, RIMBP2, Acid Metabolism, SFRS17A, SHOX2, TTC39B, TTLL9, ZNF524 Cellular Development 77 ARHGEF12, CD200R1, DOK1, FGF6, FNBP1, growth 13 23 Cell Signaling, factor receptor, Il8r, KNDC1, MRAS, MTG1, NGF, NRAS, Hematological System Ntrk dimer, P110, PDGF-AA, Pdgfr, Phosphatidylinositol 3- Development and kinase, PI3K p85, PIK3CD, PIK3R3, PLCE1, PLXNB1, Function, Cancer PTGFR, RAB3IL1, RAB3IP, Raf, Rap1, RAPGEF6, Ras, RASAL1, RASGRP2, RASSF2, RASSF4, RIN2, SSPN 78 AKR1C2, ATIC, B3GNT1, CD9, CDC7, CDH2, CDK6, COL1A2, 13 23 Genetic Disorder, COL6A1, COL6A2, COL6A3, CXCL10, Cyclin E, DBF4B, Skeletal and Muscular GAST, GGPS1, HNRPDL, Hsp27, LAMC2, MIRN362 Disorders, (includes EG: 574030), MUC4, MUC5AC, NKD2, OSR2, PCSK1, Dermatological Diseases PLAT, SCCPDH, SCG5, SERPINB5, SLC39A14, TGFA, and Conditions TGFB1, TGM1, WISP3, ZNF236 79 ATF7, ATP4B, B2M, CGA, COMMD4, COMMD8, COMMD1 13 23 Amino Acid (includes EG: 150684), CYLD, CYSLTR2, EMID2, Metabolism, Molecular ETS, FARS2, FOS, GABA, HMGB1 (includes Transport, Small EG: 3146), IL1R1, IRF8, MIRN128-2 (includes Molecule Biochemistry EG: 406916), NFATC1, NFKB1, NLRX1, PCNX, PSMC5, S100B, SLC36A1, SLC39A13, SPAG9, SPP1, STX1A, SV2B, TGFB2, TXLNB, UBE3C, VISA, ZNF503 80 ADAM10, ADAM19, ATM, CD44, CD1E, CH25H, CHI3L2, 12 24 Endocrine System DCLRE1C, DDX5, EIF2S1, ESF1, GNL1, GPR126, GPR176, Development and HMGB1 (includes EG: 3146), HNRNPD, IKK, LAMA3, Function, Nervous LAMA5, LAMB3, LAMC2, MMP1 (includes System Development EG: 4312), PLAT, PLAU, PLG, PRKDC, RELB, RTP3, SDC4, and Function, Organ SLC35E1, TMEM49, TNF, TP73, TRIM15, ZNF330 Morphology 81 ABCB10, C14ORF172, C2ORF49, CHD1L, DEM1, EIF1AD, 12 16 Cell Morphology, EXOD1, HNF4A, NOL7, SEC23A, SEC23IP, SLC22A3, Cellular Assembly and SLMO2, STAT1, STOML1, TMEM53, TRMT6, TTC22, Organization, Cellular ZAN Function and Maintenance 82 AHDC1, ATN1, ATXN1, BEND2, C17ORF81, GPATCH8, 11 18 Gene Expression, HNF4A, IL34, IPO8, KIAA0664, KIAA0913, METT11D1, Cellular Development, MIRN135A1, MIRN135A2, MIRN135B (includes Lipid Metabolism EG: 442891), MTERF, MTERFD2, NR3C1, PHPT1, RAD54L2, SLC1A6, TOX4, UNK, WNK2 83 ABCA13, ACOT8, ARNT, AZGP1, BNIP1, BTRC, CRP, DALRD3, 11 22 Gene Expression, EPO, GATA2, GATA3, GATA4, IFNAR1, KCNH6, Hematological System KIT, L3MBTL, LMO2, NOS3, NOSIP, PDLIM2, PIM1, PTPN2, Development and RINT1, SEC22B, SLC24A1, STAT1, Stat1/3, TAL1, TCF12, Function, Cellular TYK2, UBE2F, USE1, ZDHHC8, ZDHHC21, ZW10 Development 84 Arf, CTTN, Cyclin D, EPS8L1, EPS8L2, Fgfr, FGFR4, FRS2, 10 21 Cell Morphology, GAP43, Gsk3, HEY1, MCM6, MEF2, MEOX2, MTSS1, MYH11, Cellular Assembly and NEK3, NFAT complex, p70 S6k, Pak, PAX1, PDAP1, Organization, Antigen Pdgf, Pdgf Ab, PDGFB, PDGFC, phosphatase, PLC gamma, Presentation PRKD1, RPS6KB2, SAMM50, SCNM1, Sos, VAV, VAV2 85 ADAMTSL1, AGPAT4, ASCC3L1 (includes EG: 23020), 10 21 Cellular Development, ATP5B, BAT1, C17ORF85, CCNY, CFL1, CLASP1, CRTC1, Cellular Growth and CRTC2, DHX15, DYRK1B, EPB41, FBXO44, IL3, IL17A Proliferation, (includes EG: 3605), KIF1B, KRT37, MEGF10, MIRN328, Hematological System MIRN205 (includes EG: 406988), MIRN211 (includes Development and EG: 406993), NCDN, NONO, OTX2, PARD3B, RNPS1, SFRS1, Function SMCR7L, TOP2A, YWHAB, YWHAG, ZFP36, ZNF295 86 ACCN4, ARIH1, C17ORF50, C1ORF9, C20ORF111, CHST11, 10 21 Carbohydrate CTSK, EIF3M, ELK1, FATE1, HNRNPU, IFIT1, KCNAB3, Metabolism, Small MED13, MIRN349, MIRN142 (includes Molecule Biochemistry, EG: 406934), MIRN292 (includes EG: 100049711), Vitamin and Mineral MIRN298 (includes EG: 723832), MIZF, MYST3, OR8G1, Metabolism PAFAH1B1, PCGF3, SCUBE3, SEC14L4, SGK1, SLC23A2, SPATA2, STAG2, TNIK, TRAF2, WAPAL, WDR38, XKR6, ZFHX4 87 BDP1, BNC2, CBX3, CDCA7, CDK9, CDK4/6, CMAS, CSRP2, 10 21 Cell Cycle, Gene CSRP2BP, CTBS, Cyclin E, ETV3, GTF2H4, HAP1, Expression, Cancer HCG 2023776, HNRNPC, ID1, IMPAD1, MFAP1, MIRN187 (includes EG: 406963), MIRNLET7B (includes EG: 406884), MTPN, MYBL2, MYC, NEUROD1, PDGFB, RB1, RPS25, RRM2, SKP2, SLC35C1, SLITRK1, TYMS, UBAP2, ZNF691 88 CACNA1I, CATSPER1, CATSPER2, CHIC2, EDEM2, ERGIC1, 10 21 Cellular Development, GARNL1, HAND1, KITLG (includes EG: 4254), Hematological System LEF1, LRRN4, MAGEB2 (includes EG: 4113), MAGEE1, Development and MDFI (includes EG: 4188), MIRN129-1 (includes Function, Hematopoiesis EG: 406917), MIRN129-2 (includes EG: 406918), MYCN, MYF5, MYOD1, MYOG, NCL, NR4A3, PARP1, PAX3, PHOSPHO1, RABL5, RPL36A, RPS3A, TAL1, TCF3, TCF12, TCF15, USP7, ZNF423, ZNF607 89 AGTRAP, ANK1, CACNB3, Calcineurin A, Calcineurin protein(s), 10 24 Cell Signaling, CASR, DHRS9, ERLIN2, GABBR1, ITPR, ITPR1, ITPR2, Molecular Transport, JUNB, MEF2D, N-type Calcium Channel, Nfat, NFAT5, Vitamin and Mineral NFATC1, Peptidylprolyl isomerase, PGM2L1, Pkg, PLA2G6, Metabolism Pp2b, PPP3CA, RHD, SAMD4A, SCN3A, SCRIB, SPTAN1, TRP, TRPC4, TRPV6, UBE4B, VitaminD3-VDR- RXR, Voltage Gated Calcium Channel 90 ANXA8L2, AR, CCNE1, CREBBP, CYP1A1, CYP2B6 10 20 Gene Expression, (includes EG: 1555), CYP2D12, CYP4B1, ETV1, GAS7, Cancer, Cellular Growth GHRHR, HOXD1, IQCK, KAT2B, KLK2, MIRN202 and Proliferation (includes EG: 387198), MMP1 (includes EG: 4312), MYCN, NCOA3, NDN, NUCB2, PLAC2, PLUNC, PRKACA, PYCR2, retinoic acid, RPS12, SERPINA5, SERPINE1, SMARCA4, SP110, THRA, TYSND1, ZNF673 91 ATP10A, ATP11A, ATP11B, ATP5B, B4GALNT4, BHLHB5, 10 20 Cellular Growth and C10ORF2, C5ORF15, CAPRIN1, CDC25B, CDH20, CLIC4, Proliferation, Connective CRIP3, D4S234E, EPC1, FRAS1, GATAD2B, H3F3A Tissue Development and (includes EG: 3020), KIAA0907, Mg2+-ATPase, Function, DNA MIRN330, MIRN15B (includes EG: 406949), MIRN195 Replication, (includes EG: 406971), MIRN22 (includes EG: 407004), Recombination, and NEBL, PCOLCE, PPM1G, PRKD1, PURA, RAB28, SEMA6A, Repair SFRS1, SPIRE1, YWHAQ (includes EG: 10971) 92 ABCD3, AFG3L2, C1ORF38, CIDEC, CTSK, CXCL9, ERAP2, 9 20 Skeletal and Muscular FGF2, GAL3ST1, ganglioside System Development GD1b, Gsk3, IFI44L, IFNG, IL1RN, MEST, MON2, MRAS, and Function, Tissue MTMR3, MTMR4, Nos, ODC1, PI3, PRKDC, PTCD3, RNASE7, Morphology, Tissue SASH3, SDC4, SLC28A2, SPP1, TGFB2, TNFRSF11B, Development TNFRSF1A, VCAM1, ZFP57, ZFX 93 ACADS, ADAMTS2, ADSL, AQP5, CD36, CDC37, CHIA, 9 20 Cellular Movement, CLCN7, CLIC2, DCN, FGF7, FOXF1, FPR2, FUT3, GAB2, Cellular Function and GATA2, HAS2, HDAC9, HOXA9, IRF8, ISG15 (includes Maintenance, Organ EG: 53606), LTBP2, MAP4K5, NRXN1, NXPH3, PDZD2, SDC4, Development SMAD7, ST3GAL3, TGFB2, TNF, TRIP6, UACA, ZBTB11, ZNF107 94 ADM, AIFM1, BLM, CASP3, CDH2, COL4A3 (includes 9 20 Cell Death, Cell EG: 1285), cyclic AMP, deoxycholate, EBAG9, EIF2AK3, Signaling, Molecular EMCN, EMILIN2, GALC, ganglioside GD1b, GBA, Transport GNAS, HSPB2, HSPB6, MAP4K1, MLH1, NEFM, NFE2L2, PARG, PDE4A, phosphatidylserine, PRKD1, PRKDC, PSD3, RBM5, RXFP1, S1PR5, SGPL1, SGPP2, sphingosine-1- phosphate, ZC3H11A 95 BTRC, C15ORF27, CAND1, CDC34 (includes 9 20 Protein Degradation, EG: 997), CDCA3, CDK9, CUL1, CUL7 (includes Cancer, Immunological EG: 9820), Cyclin E, DDX4, FBXL2, FBXL3, FBXL21, Disease FBXO2, FBXO4, FBXO6, FBXO7, FBXO10, FBXO15, FBXO18, FBXO21, FBXO41, FBXW2, FBXW8, GCM1, GEMIN8, KIAA1045, MIRN293, MIRN34B (includes EG: 407041), RC3H2, RNF7, SENP8, SKP1, SKP2, ZC3HC1 96 ACE, ADAMTS7, CEP135, CPN2, CTSK, FGF20, FGF22, Fgfr, 9 20 Carbohydrate FGFR1, FGFR4, Frizzled, GLI2, GPR1, heparan sulfate, Metabolism, Small heparin, KLB, LSAMP, NCAM1, NCAM2, NCAN, NDST2, Molecule Biochemistry, PAX3, PPIB, PRELP, PRNP, PTPRZ1, SFRP1, SMAD9, SPP1, Cellular Development ST8SIA3, ST8SIA4, THY1, TSPY1, WNT2, ZNF587 97 Beta-galactosidase, BMP7, BTRC, COX17, DOM3Z, DYNC2H1, 9 20 Digestive System EEF1A1, EXOSC4, FAHD1, GATA4, GBA3, GLI2, Development and GLI3, HDAC9, HIPK2, KATNAL1, LCT, LEF1, MEF2A Function, Gene (includes EG: 4205), PIAS2, PIAS3, PSG9, SCAPER, Expression, Cell SENP6, SMAD2, SMAD4, SUMO1, TCTA, TMPRSS3, TRIM33, Signaling XIRP2, XPO5, XRN2, ZIC1, ZIC2 98 5′-nucleotidase, ABCA2, ABCD3, AFG3L2, Apyrase, ATP13A5, 9 20 DNA Replication, ATPase, C21ORF77, CANT1, CNKSR2, CTSK, DDX1, Recombination, and DHX8, DHX16, ENTPD1, ENTPD2, ENTPD3, ENTPD5, HEATR3, Repair, Nucleic Acid KIAA0494, KIAA1279, KIF20B, MIRN200C Metabolism, Small (includes EG: 406985), MOBKL2B, NT5C, NT5C2, Nucleoside- Molecule Biochemistry diphosphatase, PSMC1, PTP4A3, TIMM10, TIMM23, TMEM41B, TRPM3, WRNIP1, ZNF670 99 3-oxoacid CoA-transferase, ANKRD36B, ARL6IP5, 9 20 Lipid Metabolism, Small C10ORF35, CAV1, DNAH1, DNAH5, DNAH9, DNAH10, Molecule Biochemistry, DNAH11, DNAH14, DNAL4, DPY19L1, DTX4, ELOVL7, Genetic Disorder FBLIM1, FILIP1, FLNA, FLNC, GPSM2, HRAS, HSD17B8, JARID1B, MIRN31 (includes EG: 407035), MYOT, NEWGENE 1560614, NIPA2, OXCT1, OXCT2, PCYT1B, RPS6, RTP1, SCNN1A, TCTE3, TRIM54 100 ABHD5, AGPAT3, ANKRD27, C7ORF42, CETN2, DPP8, FAM153A, 9 17 Carbohydrate FAM3D, FAM83H, HNF4A, LPHN3, METAP1, Metabolism, MIRN124-1 (includes EG: 406907), MIRN124-2 (includes Dermatological Diseases EG: 406908), MIRN124-3 (includes EG: 406909), and Conditions, Genetic MIRN297-1, M1RN297-2, PLDN, PLOD3, RBM47, Disorder SFRS12, SFRS2B, SYPL1, TARBP1, YBX1, YBX2

TABLE 5 Evidence for CDK2 pathway dysregulation Gene/Protein Status Notes CDK2 Up-regulated Consistent with hypothesis (19-fold) PKCeta (PRKCH) Deleted Consistent with hypothesis Rb Unchanged Neutral; activity regulated by CDK2 via phosphorylation E2F Unchanged Neutral; activity regulated by binding of unphosphorylated Rb

REFERENCES

-   Ajona D, Hsu Y-F, Corrales L, Montuenga L M, Pio, R. 2007.     Down-regulation of human complement factor H sensitizes non-small     cell lung cancer cells to complement attack and reduces in vivo     tumor growth. J Immunol. 178:5991-5998. -   Bertotti A, Comoglio P M, Trusolino L. 2006. Beta4 integrin     activates Shp2-Src signaling pathway that sustains HGF-induced     anchorage-independent growth. J Cell Biol. 175:993-1003. -   Chin L, Garraway L A, Fisher D E. 2006. Malignant melanoma: Genetics     and therapeutics in the genomic era. Genes & Dev. 20:2149. -   Du J, Widlund H R, Horstmann M A, Ramaswamy S, Ross K, Huber W E,     Nishimura E K, Golub T R, Fisher D E. 2004. Critical role of CDK2     for melanoma growth linked to its melanocyte-specific     transcriptional regulation by MITF. Cancer Cell 6:565-576. -   Eustace A J, Crown J, Clynes M, O'Donovan N. 2008. Preclinical     evaluation of dasatinib, a potent Src kinase inhibitor, in melanoma     cell lines. J Transl Med. 6:53 -   Kashiwagi M, Ohba M, Watanabe H, Ishino K, Kasahara K, Sanai Y, Taya     Y, Kuroki T. 2000. PKCeta associates with cyclin E/cdk2/p21 complex,     phosphorylates p21 and inhibits cdk2 kinase in keratinocytes.     Oncogene 54:6334-41. -   Tetsu O, McCormick F. 2003. Proliferation of cancer cells despite     CDK2 inhibition. Cancer Cell 3:233-245.

EXAMPLE 5 Melanoma Treatment

Melanoma tumor samples and control normal tissue were obtained by biopsy. Whole exome sequencing (i.e. complete sequence of all transcribed genes) was done using commercially available Illumina technology. This method also provides quantification of individual mRNAs which provides a measure of gene expression analogous to whole genome transcription profiling.

A total of 2,802 genes from transcription profiling with a fold-change of +/−1.8 were passed on to network analysis. The filtered list of 2802 genes was subjected to an algorithm (Ingenuity Systems) that finds highly interconnected networks of interacting genes (and their corresponding proteins). The networks were integrated with the sequence data for mutated genes. Protein/protein interaction is determined directly from the research literature and is incorporated into the algorithm. These findings were then further analyzed to find particularly relevant pathways, which could provide potential therapeutic targets or, if possible, clusters of interacting proteins that could be targeted in combination for therapeutic benefit.

In addition to the dynamic networks created from the filtered genes, expression and mutation data are superimposed onto static canonical networks curated from the literature in a second type of network analysis that often yields useful pathway findings.

Before looking for potential therapeutic targets, an initial analysis was done to confirm that the global gene expression from the tumor sample was generally consistent with a priori expectations for a neoplasm of this type. The entire filtered list of genes was scored for associated cellular functions. The highest scoring category was Cancer, followed by Genetic Disorder (FIG. 18). Note also the high scoring of cell proliferative gene functions: Cell Death, Cellular Growth and Proliferation, Cell Cycle.

In addition, the networks that were assembled by the protein interaction algorithm from the filtered list of up- or down-regulated genes were analyzed with respect to the cellular and organismal functions of their individual component genes. The four top-scoring networks (with respect to the interconnectedness of their component genes) were greatly enriched in the following functions:

Cellular Assembly and Organization Amino Acid Metabolism Post-Translational Modification Cellular Movement Hematological System Development and Immune Cell Trafficking Function Protein Synthesis Protein Trafficking Genetic Disorder Small Molecule Biochemistry Cancer Skeletal and Muscular Disorders Embryonic Development

This overall pattern is consistent with what one might expect from the global gene expression of a tumor sample, as compared to normal tissue. Both of these classes of findings are consistent with global gene expression patterns of tumor tissue compared with normal tissue. These are very high-level general categories and by themselves do not point towards a therapeutic class. However they help to confirm that we are looking at signals from the tumor sample and that the integrity of the gene expression milieu has been maintained. The entire list of networks and their associated functions is set forth in Table 6 at the end of this example.

The sequence data revealed an activating V600E mutation in the B-Raf gene. This mutation is relatively common in melanoma (Goel, et al., 2006) and leads to constitutive activation of B-Raf and downstream MAP kinase/ERK signaling pathway, which in turn promotes cell proliferation (Schreck and Rapp, 2006). The pathway associated with this is shown in FIG. 19. B-Raf mutations are also associated with non-Hodgkin's lymphoma, colorectal cancer, thyroid carcinoma, and adenocarcinoma and non-small cell carcinoma of lung. B-Raf is targeted by the drug Sorafenib, approved for kidney cancer and liver cancer, however, results from clinical trials in melanoma have been somewhat disappointing (Dankort, et al., 2009). There is also a drug currently in trials that specifically targets the V600E mutation (PLX-4032). Activated B-Raf acts via phosphorylation of downstream MEK1/2 and subsequent activation of ERK1/2. ERK1/2 then acts via a variety of mechanisms to induce cell cycle progression and cell proliferation. Inhibiting activated B-Raf would thus be expected to inhibit cell proliferation.

A second common finding in melanoma patients is the loss of the tumor suppressor PTEN. While PTEN was not mutated in the tumor sample, it was down-regulated approximately 5-fold. PTEN loss has been shown to activate AKT, which is also up-regulated in the tumor with subsequent activation of the mTOR pathway, a protein kinase pathway that results in the phosphorylation of p70S6K, 4EBP, RPS6 and EIF-4B. This pathway is shown in FIG. 20. These in turn stimulate translational initiation and contribute to cell growth. Dysregulation of the mTOR pathway is implicated as a contributing factor to various cancers. Inhibition of mTOR by one of the approved inhibitors (e.g., rapamycin, temsirolimus) might be expected to overcome the loss of expression of tumor suppressor PTEN and thus be beneficial. As the case with B-Raf inhibition, however, mTOR inhibition has had clinically mixed results in various cancers.

Since both pathways converge at the level of cell proliferation, and it is known that targeting these pathways individually in patients with cancer has mixed results, it might be expected that targeting both pathways would be beneficial. In fact, because of the common co-occurrence of B-Raf mutation and PTEN loss in many melanoma patients, it has long been thought that B-Raf and PTEN might co-operate in the oncogenesis of melanoma and therefore targeting either gene individually might not be as therapeutically effective as targeting both. A recent study has shown that in a mouse model, inducing both B-Raf V600E and PTEN loss very potently produced melanoma which very closely recapitulated the human disease. Each genetic manipulation by itself was not as effective at inducing melanoma. Furthermore the tumors were effectively treated by a combination of a MEK inhibitor (MEK is downstream of, and activated by, B-Raf) and an mTOR inhibitor (mTOR is downstream of, and inhibited by, PTEN). Neither drug alone was effective (Dankort, et al., 2009). This implies that in patients with both B-Raf V600E activating mutation and PTEN loss, a combination of either a MEK or B-Raf inhibitor, and an mTOR inhibitor may be efficacious, and may explain why either drug class acting alone has had disappointing results.

Thus, the integration of two different genomic data sources revealed that two distinct but interacting pathways were dysregulated, and therefore potential targets. B-Raf was mutated but not transcriptionally regulated, and conversely, PTEN was down-regulated but not mutated. Either technology by itself (gene expression profiling or sequence data) would therefore have only indicated dysregulation of one of the pathways as a likely contributor to oncogenesis and tumor growth. Furthermore, the research literature provided an animal model validation for both these pathways being critical for melanoma development, and their combined inhibition being necessary for effective treatment. This type of integration also provides for a means of drug discovery via repurposing of existing drugs, as non-obvious findings will emerge from single patients which may be generalizable to other patients with similar pathway dysregulation.

While in the literature report described above a MEK inhibitor was used to inhibit the B-Raf pathway downstream of B-Raf itself, there are currently no approved MEK inhibitors, although trials are ongoing. It may therefore be possible to use a B-Raf inhibitor in combination with an mTOR inhibitor (which is also currently approved).

Although we did not see much up-regulation of genes downstream of B-Raf or PTEN, the effects of both of these genes on their downstream pathways occur at the level of protein phosphorylation and would not necessarily be expected to be reflected in changes in the mRNA levels of these downstream effectors.

In summary, the tumor sample displayed both an activating B-Raf V600E mutation, and down-regulation of tumor suppressor PTEN. Both of these pathways lead to cell proliferation, but targeting each individually has mixed results in cancer patients. There is literature evidence that targeting both of these pathways simultaneously may be effective in treating melanoma. Furthermore, there are approved drugs available which target both B-Raf (sorafenib), and mTOR (e.g., rapamycin) which is a downstream effector of PTEN loss.

In the foregoing examples, a mutation in the NRAS gene which could have activated the PI3K pathway and thus suggested combination treatment. However, this mutation was not characterized as to whether it would result in a loss or gain of function or neither. Since the mutation was not characterized, expression data were necessary to result in the suggestion of combination treatment.

These conclusions are validated by studies elucidating B-Raf pathway dysregulation at the protein level by determining whether downstream effectors of B-Raf are hyper-activated using immunohistochemistry of tumor sections and control tissue using phospho-specific antibodies to ERK1/2 and MEK, downstream effectors of B-Raf with respect to cell growth and proliferation, wherein hyperphosphorylation of ERK1/2 and MEK indicate over-activation by B-Raf and supports the validation of B-Raf as a target. PTEN at the protein level is also elucidated by determining whether downstream proteins normally inhibited by PTEN are hyper-activated. This can be done using immunohistochemistry of tumor sections and control tissue using phospho-specific antibodies to AKT and P6SK, pro-proliferative kinases in mTOR pathway normally inhibited by PTEN, wherein hyperphosphorylation of AKT and P6SK indicate over-activation of mTOR as a result of PTEN loss and supports the validation of mTOR as a target.

These are the complete networks of interacting genes generated from the filtered list of up- and down-regulated genes. The Score column represents the overall level of interconnectedness within each network, and the Top Functions column describes cellular functions that are over-represented (relative to chance) in each network, as determined by the individual annotation of each gene in the network.

TABLE 6 Regulated Networks from Tumor Sample Focus ID Molecules in Network Score Molecules Top Functions 1 C6ORF48, CACYBP, CAD, CFTR, COG2, COG3, COG8, DHX29, 31 35 Cellular Assembly and DLG5, FAT1, GCC1, HNRNPU, HSPA6, IL1RAPL1, Organization, Amino Acid KDELR2, MLF1, MUC2, NDN, PDZK1, PHGDH, PHLDA2, Metabolism, Post- RCN1, SDF2L1, SEC61A1, SEC61G, SH3BGRL2, SLC1A5, Translational Modification SRP68, SRPR, SYNGAP1, TBC1D17, TNFRSF1B, TXNDC11, XBP1, XPO1 2 ABTB2, BTN3A3, C11ORF82, CHST4, CTNNA-CTNNB1- 30 34 Cellular Movement, CTNNG-CDH5, CTSF, DHRS3, ENDOG, FOXF2, KIF20A, Hematological System LAMP3, LSS, MCOLN1, MCOLN3, NFRKB, NINJ1, PDZD2, Development and PEMT, PLD3, PRKRIR, PXMP2, RASA3, SCUBE2, SERPINB8, Function, Immune Cell SLC14A2, SLC16A5, SLC1A4, SLC28A2, TDRD7, Trafficking TMEM49, TNF, TNFRSF21, UACA, ZNF330, ZNF365 3 AKR1B10, ANKRD35, ATXN10, C19ORF70, CDR2, CIRBP, 30 34 Protein Synthesis, Protein COL24A1, EHD4, FAM113A, FOLR2, GSTK1, HIVEP3, Trafficking, Genetic HRK, JUN, LXN, MAFF, MAGEA6, MTHFR, OSTC, PHACTR3, Disorder PSPH, RPN2, SEC63, SERP1, SLC25A22, SNRPA1, SRPRB, SSR4, STYXL1, SVIL, TCF20, Tcf1/ 3/4, TNFRSF14, TSC22D1, VEPH1 4 ACTN4, ANKS1A, ASPM, CHCHD10, COL6A2, CRIP1, DZIP3, 30 34 Amino Acid Metabolism, EMP3, ERBB2, ESPL1, IRX3, KRT7, LTBP3, MFNG, Post-Translational MICALL2, NPNT, NPTX2, NUCB2, P4HA1, P4HA2, P4HB, Modification, Small PDLIM4, PINK1, PMEPA1, PQLC1, Procollagen-proline Molecule Biochemistry dioxygenase, RAB18, SEMA7A, SH3BGRL, SHROOM3, SPAG4, SRPX, ST3GAL4, TAX1BP3, WFS1 5 AASS, BMI1, CBX2, CBX7, CELSR2, COLEC12, Dolichyl- 30 34 Cancer, Skeletal and diphosphooligosaccharide-protein Muscular Disorders, glycotransferase, EXOC1, FAM45A, HIST1H4C, HIST2H2AA3, Embryonic Development HIST2H2BE, IGF2BP2, KIAA1267, KIAA1797, KLF6, MYCBPAP (includes EG: 84073), NECAB2, PCGF6, PHF10, PHF19, PRRG2, PRUNE, PXN, RRBP1, SERPINH1, STT3A, TBL1XR1, TES, TNS1, TRIM9, TUSC3, U2AF1, VCL, VSX2 6 ABL1, Adaptor protein 1, ANKRA2, AP1M1, APC, BST2, 30 34 Gene Expression, Cancer, C20ORF46, CADM1, CBX3, CBX5, CHPF, CRTAM, DDB1, Gastrointestinal Disease DIAPH1, DOK3, EPB41L3, FAM126A, GGA2, HDAC4, HPRT1, KIF15, LRP3, MKI67, NECAP2, NR2C1, NXPH3, PIGR, PIN1, PRTFDC1, PVRL3, SAE1, SH3BP2, TNFRSF11B, WDR40A, ZIC2 7 ACTR10, ACTR1A, ANKS1B, CELSR3, DCTN2, DCTN3, 28 33 Cellular Function and DST, DYNC1H1, DYNC1I1, Dynein, DYNLRB1, DYNLRB2, Maintenance, Cellular DYNLT3, FAM96B, INA, KHDRBS3, KNTC1, LDB3, Assembly and MYBPH, MZF1, NEFL, NIN, P38 MAPK, PDXDC1, PHLDA3, Organization, Nervous PNO1, RGMA, SAAL1, SFRS9, SGSM2, SPTBN2, TESC, System Development and TNNC2, TRA2A, ZW10 Function 8 AP3B1, AP3M1, ARNTL, BHLHB, BHLHE40, BHLHE41, 28 33 Cellular Assembly and BUB1, CBLC, ETV5, KCNK3, LHX6, LMCD1, Mapk, MARCH2, Organization, Cellular NEUROG3, NKX2-1, OLIG2, PTCRA, PTPRH, PTRF, Growth and Proliferation, SFTPC, SGOL1, SPRY4, STRA13, STX7, STX8, THBS2, Nervous System TPD52L1, TRHR, VAMP8, VPS16, WDR91, WWTR1, XPOT, Development and Function ZNF148 9 BLOC1S1, BRWD1, CDCA7L, CORO2A, CPS1, DEAF1, 28 33 Gene Expression, Cell DUSP22, ELMOD2, FASTKD5, HOXB1, ING3, KIAA1279, Signaling, Amino Acid MAL, MED4, MED25, MED30, NR3C1, PARP4, PLEKHF1, Metabolism PSMG2, RABGGTB, RRAGC, SEMA5B, SLC38A1, TADA1L, TADA2L, Taf, TAF5L, TAF6L, TELO2, TNFAIP1, TPST2, TRAP/Media, TRRAP, WDR40B 10 ABHD6, Adenosylhomocysteinase, AHCY, AHCYL1, AHCYL2, 28 33 Carbohydrate Metabolism, APEH, APOBEC3B, CMBL, CTAGE5, DCPS, DCXR, Small Molecule EXOSC4, FAHD1, Hydrolase, IER3IP1, IMPA2, KCTD12, Biochemistry, RNA LRRC8D, MLEC, MRTO4, NHP2L1, NLN, PFAS, PPM1G, Damage and Repair PTPN7, REXO2, RHPN2, STRADB, TARSL2, TMEM51, TMPRSS3, TRAF6, TSEN15, TUBB6, ZRANB1 11 C17ORF70, CCL5, ECEL1, EI24, FANCD2, FANCL, FAR1, 28 34 Endocrine System FOXC1, HOXA5, HOXB3, HOXD12, IER2, KLF10, MEIS1, Development and MEN1, MLKL, NELF, NKX2-5, PBX1, PBX2, PHC1, PTGDS, Function, Organ RAD51AP1, RNA polymerase II, SETD2, SFMBT1, Development, Organismal SLC9A8, SMARCD3, SMYD3, TARBP1, TBX1, TBX2, TSPAN7, Development ZDHHC7, ZNF83 12 ACAA2, AGPAT9, ANXA5, Apyrase, ASB13, EIF3A, EIF4EBP1, 27 33 RNA Damage and Repair, EIF4G1, ENTPD3, ENTPD6, ENTPD7, F5, FGFR3, FGFR4, Organ Development, GPRIN2, GRAMD3, HOXC9, KIAA0247, LAMP2, Respiratory System LDLRAD3, LIF, LOXL4, Nucleoside-diphosphatase, NUDT9, Development and Function OSBP2, PROS1, SELENBP1, SERTAD2, SMG7, STAMBPL1, TMCC2, TRIP13, UPF2, VWF, ZFP36 13 AFF3, BAT2, C4ORF14, COL9A1, COL9A2, DDX20, DDX47, 26 32 RNA Post-Transcriptional DICER1, EIF2C2, H1F0, HSP90AB1, HSPH1, HYOU1, Modification, Cancer, Importin beta, IPO8, IRS1, L-lactate dehydrogenase, Ldh, Respiratory Disease LDHA, LDHAL6A, LDHAL6B, LDHC, LSM10, NFIB, NPAS2, PES1, PIWIL4, RAD51L1, SCML2, SND1, SNRPD1, SNRPF, SNRPG, STARD13, TNRC6B 14 BAAT, COL15A1, Delta/Jagged, DLL1, DLL3, EHD1, FBLN1, 26 32 Cellular Development, GIPC1, GRB10, HES1, HES6, HYAL2, ID2, IGF1R, KCNA3, Cardiovascular System KCNAB2, KLF2, LAMA4, MEGF10, MFAP5, MIB2, Development and NOC2L, Notch, NOTCH2, NOTCH4, NOV, OLIG1, RPL24, Function, Tissue SDC4, SH3BP4, SLC7A8, SLCO2A1, TFPI, Troponint, Morphology TYMS 15 ADAM19, Alpha catenin, ASAP1, BXDC2, C20ORF30, CAM, 26 32 Cell-To-Cell Signaling and CLDN7, CLDN12, CNTNAP2, CTNNA1, DDX27, DHX57, Interaction, Cellular DKC1, GNL3, KIF14, LYAR, MARCKSL1, MPDZ, NEDD9, Assembly and OSTF1, PACSIN3, Pseudouridylate synthase, PSTPIP1, Organization, Cancer PTK2B, PTPN3, PUS1, PUS3, RPL28, RPUSD4, SH3KBP1, SRP14, TJP2, TJP3, TRUB1, VEZT 16 AATF, CAMP, CCL2, CHGB, CSPG4, DAPK3, DNA-directed 26 32 DNA Replication, DNA polymerase, EYA1, EYA4, GTF2F2, HMGB2, KIAA0101, Recombination, and MPHOSPH6, MPZ, PAFAH1B3, PAWR, PKM2, Repair, Cellular Assembly PLEKHM1, PMP22, POLB, POLD1, POLL, POLQ, POU3F1, and Organization, Nervous REV3L, SIX1, SIX4, SIX5, TFIIF, TFIIH, TLE1, TLE3, TNFAIP2, System Development and UTX, XRCC1 Function 17 Alcohol group acceptor phosphotransferase, ARID2, ARID1A, 26 32 Cell Cycle, Amino Acid ARID4B, BRAF, CDK8, EFCAB6, EVI1, GRK4, GSPT1, Metabolism, Post- HDAC1, LIMK1, MAD1L1, MAP2K3, Map3k, MAP3K6, Translational Modification MAP3K8, MAPK9, MECP2, MEKKs, NDC80, NEK2, PAK1, PAK2, PCTK3, PRKCQ, PRKX, RCOR2, SALL1, SALL4, SAP18, SMARCE1, SPC25, TBX3, TTK 18 AGL, ALP, ANK3, BMP, BMP4, BMP7, BMPR2, C10ORF54, 26 32 Cell Signaling, Cell COL7A1, CXORF15, DYNC2LI1, EID2, FST, ID1, ID3, JPH1, Morphology, Cellular KLHL9, KRT2, LAPTM5, NEDD4L, NELL1, PAX9, PDZRN3, Development PYCR2, RASD2, RSPH3, RUNX2, Smad, SMAD3, ST6GALNAC2, TAGLN, TWSG1, ZC3H7A, ZEB2, ZMIZ1 19 ADAMTS2, ADAMTS4, ADAMTS7, APH1B, ATG4C, BIRC6, 26 32 Protein Degradation, BLMH, BMP1, CASP4, CASP6, COL4A1, COMP, CSF1R, Connective Tissue CSF2RB, CTSD, Cytochrome c, DIDO1, FAP, GRN, GZMB, Disorders, Dermatological ICAM5, JUP, LTA4H, MDN1 (includes EG: 23195), Diseases and Conditions MT1A, PCSK7, peptidase, PSEN2, PSMB5, RNF150, UBE2, UBE2E2, UBE2F, UBE2L6, UBE2S 20 CITED2, CMPK1, CNPY2, DSTYK, DUB, FIBP, GZMM, ITIH5, 26 32 Behavior, Digestive KIF23, LHX2, MIF, MSRB2, MYCN, NMU, NMUR2, System Development and OTUB1, PARP1, PDE4DIP, PLA2, PLA2G2A, PLA2G4D, Function, Cell Morphology RASGRF1, TGM2, TMEM87B, TNIK, UCHL1, UCHL3, Uridine kinase, USP6, USP8, USP10, USP48, USP53, USP54, ZFAND5 21 ANK1, BRPF1, CA2, CA3, CA7, CA14, CA5B, Carbonic 26 33 Genetic Disorder, Renal anhydrase, CKS2, COPS6, CRELD1, CSRP1, CSTF3, CTDSPL, and Urological Disease, DLGAP5, DRAP1, FBP1, Fructose 2,6 Bisphosphatase, Infectious Disease GGT1, HEXB, MAP7D1, MXD4, PFKFB2, PFKL, PFKM, PGM1, PLOD1, PTEN, PTPRZ1, PYGL, SLC4A2, SLC4A3, STK40, SURF4, TCP10L 22 ADAMTS5, AIF1, ALT, ANXA9, CARD8, CASP1, CASP5, 26 32 Lipid Metabolism, Small Casp1-Casp5, CEBPG, CHST6, CP, F13A1, GM2A, GPT2, IFT57, Molecule Biochemistry, IL1B, IL1F7, ITPA, LCP1, MIA, NALP, NLRP1, NLRP2, Genetic Disorder NLRP6, PELI1, PLA2G7, PLB1, PPHLN1, RARRES2, RNASE7, SLC25A25, SMPD1, SRGN, TPSD1, TYMP 23 3′,5′-cyclic-nucleotide phosphodiesterase, ARL2, BASP1, 26 32 DNA Replication, Calmodulin, CAMK1, CAMKK2, CCT2, CCT5, CDYL, DHX38, Recombination, and FAM10A4, IQCB1, LIMA1, LNX2, MAP3K3, MYO1B, Repair, Nucleic Acid MYO1D, NRGN, NUMBL, Pde, PDE1B, PDE2A, PDE5A, Metabolism, Small PDE6B, PDE6D, PDE7B, PDE8B, PPP4C, RAI14, RIPK3, Molecule Biochemistry RPH3AL, SCIN, TRPM2, TRPV4, UNC13B 24 APPL2, BAT5, C19ORF10, DTX3L, GPRC5C, IFI27, IFIT2, 25 32 Gene Expression, Lipid IFIT3, IFITM1, Immunoproteasome Pa28/20s, KPNA5, LY6E, Metabolism, Small NCAPD3, NCAPH2, ODF2L, PARP9, PDLIM2, PILRB Molecule Biochemistry (includes EG: 29990), PRSS23, PSMA2, PSMA6, PSMB, PSMB7, PSMB8, PSME2, RNF5 (includes EG: 6048), SCP2, Soat, SOAT1, SOAT2, SP110, STAT1, STAT2, TMEM147, TMEM222 25 ABL2, ALOX5AP, Ant, Basc, BCL2L1, BCL2L10, BLM, BNIPL, 25 31 Cellular Function and BRIP1, CEBPZ, CHEK1, CHTF18, DSCC1, ERCC1, Maintenance, DNA Mre11, PAXIP1, RAD50, RAD9A, RFC2, RFC3, RPA, RPA3, Replication, RTKN, SEMA6A, SIVA1, SORBS2, SRPK2, TERF2, TERF2IP, Recombination, and TIMELESS, TIMP4, TP53BP1, WISP1, XRCC5, XRCC6 Repair, Cell Cycle 26 ADARB1, ADD3, AXIN2, CCNO, CTH, CXCR7, DTNA, ELOVL2, 25 31 Amino Acid Metabolism, GDF15, GHR, GREM1, growth factor receptor, HSD3B7, Small Molecule KCNJ12, MCFD2, MT1E, MTHFD2, NOX4, PDGFBB, Biochemistry, Cell Death PDGF-AA, PLEKHA1, PRRX2, PSAT1, RND3, SLC1A1, SNTA1, SNTB1, SNTB2, SNURF, Sphk, SYNC, SYNE1, TRIB3, UCK1, VAPB 27 ASB9, C4ORF17, CDC14B, CKB, CKM, Creatine 25 31 Small Molecule Kinase, ENO1, ENO2, Enolase, GTF2IRD1, HERC5, KRT78, Biochemistry, Cell-To-Cell MAP1B, MEF2C, MYOM2, NEBL, NGF, RANBP1, ROR1, Signaling and Interaction, RUSC1, SCG2, SERPINF, SERPINF1, SERPINF2, SH3PXD2A, Cellular Assembly and SIGIRR, SIRT2, SNX22, STK38, SYNPO2, TRAF3IP1, Organization TUBA4A, Tubulin, VGLL4, ZYX 28 AKAP6, ALS2CR12, BCLAF1, Cdc2, CLK1, Cyclin B, DARS, 25 31 Gene Expression, Cell DNA-directed RNA polymerase, FKBP5, FKBP6, FKBP11, Cycle, Cell Morphology FLNB, GALK1, HMG20B, KIF4A, LATS2, LPHN1, LPHN2, Peptidylprolyl isomerase, PIK3R1, PIN4, POLR1B, POLR1E, POLR2D, POLR2F, POLR3A, PPIG, PPIL3, QRICH1, ROS1, SFRS4, SHANK2, TACC2, TAF1C, TCOF1 29 BCL10, BIRC3, BIRC8, BRCA1, C11ORF9, C9ORF89, CARD10, 25 31 Cell Death, Cancer, Cell Caspase, CCNG1, CD80/CD86, CHST1, CTCFL, DLAT, Cycle E3 RING, HERPUD1, HIST1H2BC, KHDC1, LSP1, MLXIP, MX1, NT5DC2, NUFIP1, P2RX1, PDCD6, PKC(&beta;, &theta;), PLAG1, PLSCR4, STC1, SUGT1, TNFRSF18, TNFRSF19, TRAF1, WEE1, WIT1, ZNF350 30 CD34, CRYAB, CTSL2, CUBN, DUSP6, Dynamin, ENC1, FSH, 25 31 Organ Development, FSHR, GATA2, GPRC5B, HAS2, hCG, HPSE, HSD17B, Reproductive System HSD17B1, HSD17B4, HSPB2, LHCGR, LRRC32, MAMLD1, Development and MARCH3, MT1H, MT1X, MYO1E, PLA1A, RGS5, SETX, Function, Developmental SLC20A1, STEAP1, STK17A, TF, TFRC, TSHB, VGF Disorder 31 ADRB2, BSCL2, C9ORF46, CHD3, Ck2, Clathrin, COL1A1, 25 31 Cancer, Tumor COL1A2, CSPG5, CUX1, DNASE2, DRD5, DSPP, FAM134A, Morphology, Molecular FAM176A, FAM86C, Gs-coupled Transport receptor, Hat, IRF4, NEIL2, ODC1, PAM, PLA2R1, PRDX5, RBM17, SAT1, STARD10, TGFB3, TNP1, TOP2A, TSC22D3, TSPAN13, UBQLN3, YBX1, YBX2 32 APP, ATOX1, ATP7A, C9ORF75, CLIC1, CLSTN1, ETHE1, 24 33 Cellular Function and EVI5L, FAM160A2, GLUL, HSD17B14, KCTD13, Na-k- Maintenance, Small atpase, NUDT18, PCBD1, PDIA6, PERP, Plasminogen Molecule Biochemistry, Activator, PPME1, PPP1R13B, RAB38, RAB33A, RABAC1, Molecular Transport RENBP, RTN2, RTN3, SDPR, SNX15, SORL1, SPON1, SRGAP3, TM2D1, TP53BP2, TP53I3, ZBED1 33 ALAD, ARID1B, BBS1, BBS7, CCL8, CCL23, CCR1, EDEM1, 24 31 Cellular Assembly and FLOT1, GNA14, HES5, HSCB, IFI30, IFI35, IFITM2, IL23, Organization, IL24, IL11RA, IL20RA, MAN2A1, MAN2A2, Mannosidase Developmental Disorder, Alpha, NIPSNAP3A, NMI, PCM1, PCNT, PLP2, RNASE1, Genetic Disorder RRP12, SOX10, STAT3, STAT1/3/5/6, THY1, TRIP10, WASP 34 ANTXR1, ATF5, BATF, BATF3, BCL11A, BRD7, CEBPB, 24 31 Gene Expression, Cancer, CREB5, CREB3L4, Ctbp, DBP, DDIT3, DFF, ELL3, F8, F8A1, Hematological Disease FOSB, FTH1, HIPK2, LCN2, MAGEH1, NFIL3, NR4A2, ORM1, PER3, Pias, PIAS3, PML, SATB2, SLK, TDG, TM4SF1, Top2, TP53INP1, UPP1 35 ACP1, AK3L1, ARSB, ARSG, ARSH, ARSJ, ARSK, Aryl 23 30 Cellular Movement, Sulfatase, BCAT2, CST2, ENSA, GART, HEY2, HIF3A, JAG1, Skeletal and Muscular KCTD15, NCK, Pak, Pdgf Ab, PDGF- System Development and CC, PDGFC, PDGFD, PDGFRB, PPA1, PPP1R15B, SEMA3B, Function, Gastrointestinal SMTN, ST5, STARD8, SULF1, SULF2, SYNM, TBC1D8, Disease VEGFA, WIPI1 36 Acid Phosphatase, ACP5, ACP6, AHCTF1, CENPF, CPNE2, 23 30 Molecular Transport, RNA CPNE4, DHCR7, IBSP, KNDC1, LIMS1, LIMS2, Mi2, MYCBP2, Trafficking, Cellular MYO1C, NUP133, NUP160, NUP214, NUPL1, PARVG, Growth and Proliferation PKC (&alpha;, &beta;, &gamma;, &delta;, &epsilon;, &iota;), POU3F2, POU3F4, PQBP1, RAD21, Ras, RASAL1, RDX, RGS10, RSU1, SEC13, SGPP1, SMOC2, Tap, TM7SF4 37 ABCC6, ABCD3, ACTC1, adenylate kinase, AFG3L2, AK1, 23 30 Energy Production, AK5, ATP11B, ATP13A5, ATP1B1, ATP5D, ATP5G1, ATP6V0A2, Nucleic Acid Metabolism, ATP6V1E2, ATP6V1G2, ATP6V1H, ATPase, BAX, Small Molecule COX7A1, CTSK, Cytochrome c oxidase, ETNK2, H+- Biochemistry exporting ATPase, H+-transporting two-sector ATPase, HTR2B, MFN1, MYH1, MYH6, MYL1, MYO9B, NOMO1, PSMD6, SMARCA4, TNNI2, TRIM63 38 ACACB, AMPK, ANGPT2, ANP32B, APOB, CCNG2, CDH1, 23 30 Cellular Function and CSNK2A2, FKBP4, GTSE1, Hsp27, HSP90AA1, HSPA2, Maintenance, Cellular HSPA5, HSPA1A, HSPA1B, KCNMA1, KLK3, KLK6, LOXHD1, Compromise, Cell-To-Cell NAP1L4, Ndpk, NME1, NME2P1, Nos, PA2G4, PRKAA, Signaling and Interaction RBM4B, RPL30, RRM2, TMEM132A, TTLL4, TXNDC3, ZBTB33, ZEB1 39 3 BETA HSD, ALOX5, ASCC2, ASS1, CHUK, CLEC11A, 23 30 Cell Signaling, Cell- DHH, EGR2, EGR3, FAM118B, GJB1, GLI1, Gli-Kif7- mediated Immune Stk36-Sufu, GMFG, HHIP, IDS, KIF7, LASS4, Mek, NAB1, Response, Cellular NCL, NDRG1, NXPH4, Patched, PPP2R2B, PPT2, S100A9, Development SELE, SH3D19, SHMT2, SNX10, STK36, Tnf receptor, TNFSF13B, TUBB4 40 ALS2CR11, BRD8, CCDC5, CCDC76, CDC45L, CDCA4, 23 32 Cell Cycle, DNA CDT1, CREG1, CSDE1, DUOX2, E2f, E2F2, EP400, ERRFI1, Replication, HIST1H3H, L3MBTL, MCM3, MCM4, MCM5, MCM6, Recombination, and MCM7, MCM10, MCPH1, MTSS1, NASP, NOM1, NRD1, Repair, Gene Expression NUSAP1, OIP5, Pdgf, PNKP, Rb-E2F transcription repression, SETD8, TCEB3B, ZBTB43 41 ADH4, AIFM1, BSN, C6, C8, CAPN1, CAPN6, CAPN9, CAST, 22 31 Lipid Metabolism, Small CDK5R1, CDK5R2, CTSC, CYB5R1, CYB5R2, DHRS1, Molecule Biochemistry, DHRSX, Electron-transferring-flavoprotein dehydrogenase, Vitamin and Mineral ETFDH, IRF2, LPA, MAPRE2, MLPH, Oxidoreductase, Metabolism P2RY2, PDIA5, RDH, RDH5, RDH8, RDH11, RETSAT, RIMS1, SLC35C2, SLC9A3R1, TBC1D10A, VCAM1 42 AHSG, AKT1, ARL15, BPGM, BRSK1, C1QC, CCDC106, 22 30 Nucleic Acid Metabolism, CDCA3, CRMP, CRMP1, DDX18, Dihydropyrimidinase, DPYS, Cell Morphology, Renal DPYSL3, DPYSL5, FES, FXYD5, HNRNPH3, KIAA1199, and Urological System LRRC1, MICAL1, MYT1, NUAK1, PKC ALPHA/BETA, Development and Function Plexin A, PLXNA1, PLXNA2, PLXNA3, PRPSAP1, Sema3, SEMA3C, SEMA3F, SIK1, STRADA, UBE4B 43 CABLES1, CDK2, CKS1B, Glutathione peroxidase, Glutathione 21 29 Cell Morphology, Genetic transferase, GMFB, GPX8, GSTA2, GSTM1, GSTM2, Disorder, Hepatic System GSTM4, GSTO1, GSTO2, GSTP1, GSTT1, IGSF21, Laminb, Disease LMNA, LMNB1, MGST2, MGST3, MRFAP1L1, MYH8, NPDC1, PDCD11, PKC (&alpha;, &beta;, &epsilon;, &gamma;), PKC (&alpha;, &epsilon;, &theta;), PRKCA, PRKCH, RAB17, S100A8, Sod, TAGLN2, TSNAX, UNC84A 44 ALDH2, ANAPC13, APC, AURKA, BCKDHA, BCKDHB, 21 29 Cell Cycle, Embryonic CD3EAP, CDC20, Creb, CSN3, ETV1, FBXO5, GALNT3, GALNT8, Development, Cell- GALNT11, GALNT12, GALNTL4, GRIN, GRIN2C, mediated Immune GRIN2D, HSPE1, KRT1, MAPK11, MSK1/2, MUC5AC, Response NOX5, PKMYT1, Polypeptide N- acetylgalactosaminyltransferase, PRKD1, PTTG1, RPS6KA2, RPS6KA4, Rsk, STEAP3, UBE2C 45 Ahr-aryl hydrocarbon-Arnt, ANXA4, ARMET, CYP1A1, CYP2J2 21 29 Cardiovascular Disease, CYP4A22, CYP4F11, CYP4F12, ELF4, EPHX1, GPC1, Genetic Disorder, MYO10, NADH dehydrogenase, NADH2 dehydrogenase, Neurological Disease NADH2 dehydrogenase (ubiquinone), NDUFA4L2, NDUFB6, NDUFB7, NDUFB8, NDUFC2, NDUFS3, NDUFS4, NDUFS5, NDUFS7, NDUFS8, NDUF V1, NDUFV2, NDUFV3, NOS3, NOSTRIN, Nuclear factor 1, TACC3, TRIP11, TUBA1A, Unspecific monooxygenase 46 ADPGK, AKAP12, ALPHA AMYLASE, AMY1B, AMY2B, 21 29 Cell Cycle, Cancer, CCNE1, CD38, CDKN1C, CTSH, Cyclin A, Cyclin D, Reproductive System Cyclin E, DMXL1, E2F5, EPAS1, FRAP1, GAL3ST1, GBE1, Disease IL6R, LGALS3, LYZL1, MB, MEF2, NRAS, NRN1, NUDT10, OMA1, Pld, PLEK2, PRR5, RASSF1, RIN2, SKP2, SLC16A3, ZBTB17 47 14-3-3, ADARB2, CRABP2, CXCL10, CXCR4, DHDDS, EMID1, 21 29 Embryonic Development, GAD1, GPR1, IL17R, IL17RA, INTS3, LCT, LRRC37A3, Tissue Morphology, MAP2K1/2, MARK1, MYH2, NRIP1, PAX3, PRAME Dermatological Diseases (includes EG: 23532), PRDM5, PSG2, PTPRJ, RARA, RARB, and Conditions Rbp, RBP5, RBP7, SLC26A4, STAT5a/b, SYNCRIP, TG, TGM1, TNFRSF10B, Transferase 48 ABR, ARHGAP27, ARHGDIB, Arp2/3, BAIAP2, CNTN1, 21 29 Cellular Assembly and CNTNAP1, DEF6, DOCK2, EFNB3, EPB41L2, EPS8, EVI5, Organization, Nervous FCGR1A/2A/3A, Integrin alpha V beta 3, ITGB5, PARD6G, System Development and Phosphatidylinositol4,5 kinase, PIP4K2A, PIP5K1A, Plexin Function, Cell Morphology B, PLXNB1, PREX1, PTPRB, Rac, RAC1, RAC2, RAPGEF1, RASSF2, RGL2, RIT1, RRAS, SEMA4D, TIAM2 (includes EG: 26230), TNK2 49 BGN, BMS1, C2, C1q, C1QA, C1S, CCNB2, Collagen(s), Complement 20 30 Cell-To-Cell Signaling and component Interaction, Connective 1, DCN, FBN1, FMOD, FSTL1, Igm, KIAA1191, LAMA3, LSM4, Tissue Development and NCR3, NOL12, PLEKHA5, PTS, RCBTB1, RORC, SF3B3, Function, Skeletal and SFRS15, SFTPD, SLC25A38, SNRNP70, SNRPB2, SNRPN, Muscular System Tgf beta, TGFBI, TLL1, TLL2, WBP4 Development and Function 50 B4GALT1, B4GALT4, B4GALT5, B4GALT6, CD9, CD81, 20 28 Dermatological Diseases CDC42EP5, CSF3R, DUSP10, DUSP16, Erm, ETV4, Galactosyltransferase and Conditions, Genetic beta 1,4, Gm-Csf Receptor, GPR56, Integrin Disorder, Carbohydrate alpha 3 beta 1, Jnk, KLHL2, LGALS8, MSN, MT1G, MTF1, Metabolism NET1, phosphatase, PI4KA, PRDX4, PTGFRN, RNASEL, ROR2, SACM1L, TMC6, TMC8, TNN, TSPAN, TSPAN4

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1. A method to identify at least one therapeutic target in an individual cancer patient, which method comprises: (a) ascertaining characteristics of the genome and/or characteristics of the molecular phenotype in a biopsy of the cancer afflicting said patient to obtain one or more first data sets and assaying said characteristics in normal tissue to obtain one or more second data sets; (b) identifying differentiated characteristics in said one or more first data sets which differ from those in said one or more second data sets; (c) ascertaining one or more pathways associated with said identified differentiated characteristics; and (d) identifying at least one therapeutic target associated with said one or more pathways.
 2. The method of claim 1, wherein the identifying in step (b) and/or the ascertaining in step (c) is performed by a computer.
 3. The method of claim 1, wherein the ascertaining in step (c) is performed by accessing a database describing pathways exhibited by gene products and matching said pathways to the differentiated characteristics.
 4. The method of claim 1, wherein said pathways are metabolic pathways and/or signal transduction pathways.
 5. The method of claim 1, wherein the normal tissue is obtained from the same patient and/or from the same organ as the tumor tissue.
 6. The method of claim 1, wherein characteristics of the genome are selected from the group consisting of single nucleotide polymorphisms (SNPs), copy number variants (CNVs), loss of heterozygosity (LOH), gene methylation, and sequence information.
 7. The method of claim 1, wherein characteristics of the molecular phenotype are selected from the group consisting of overexpression or underexpression of genes, proteomic data, and protein activity data.
 8. The method of claim 1, which further includes triangulating any data set to assess validity of the ascertained pathways as compared to noise.
 9. The method of claim 8, wherein said triangulating applies one or more of algorithms 1a, 2a, 3a, 1b, 2b and 3b.
 10. The method of claim 1, wherein more than one data set is obtained from each of said cancer biopsy and normal tissue and said method further includes integrating the identified differentiated characteristics to identify patterns that are ascertainable only from concurrent visualization of multiple characteristics.
 11. The method of claim 1, which further includes extrapolating the identified differentiated characteristics to characteristics that have not been measured.
 12. The method of claim 1, which further includes designing a treatment protocol using drugs and/or biologics that interact with said at least one target.
 13. The method of claim 12, which further includes formulating said designed treatment protocol into pharmaceutical compositions.
 14. The method of claim 12, which further includes treating said patient with the designed treatment protocol.
 15. The method of claim 12, which further includes applying the identified differentiated characteristics and treatment protocol design to diagnostic and therapeutic discovery protocols. 